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Merge pull request #23618 from gambletan/fix/file-to-input-file-mapping
fix: map Chat Completion file type to Responses API input_file
This commit is contained in:
Cesar Garcia
GitHub
commit
501ddb428c
2 changed files with 313 additions and 110 deletions
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@ -240,10 +240,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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if key in ("max_tokens", "max_completion_tokens"):
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responses_api_request["max_output_tokens"] = value
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elif key == "tools" and value is not None:
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responses_api_request[
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"tools"
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] = self._convert_tools_to_responses_format(
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cast(List[Dict[str, Any]], value)
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responses_api_request["tools"] = (
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self._convert_tools_to_responses_format(
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cast(List[Dict[str, Any]], value)
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)
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)
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elif key == "response_format":
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text_format = self._transform_response_format_to_text_format(value)
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@ -696,6 +696,20 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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verbose_logger.debug(
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f"Chat provider: image -> {converted}"
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)
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elif item_type == "file":
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# Map Chat Completion file to Responses API input_file
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# {"type": "file", "file": {"file_data": "...", "filename": "..."}}
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# -> {"type": "input_file", "file_data": "...", "filename": "..."}
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file_data = item.get("file", {})
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converted = {"type": "input_file"}
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if isinstance(file_data, dict):
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for key in ["file_id", "file_data", "filename"]:
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if key in file_data:
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converted[key] = file_data[key]
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result.append(converted)
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verbose_logger.debug(
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f"Chat provider: file -> {converted}"
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)
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elif item_type in [
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"input_text",
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"input_image",
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@ -1058,9 +1072,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
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)
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if provider_specific_fields:
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function_chunk[
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"provider_specific_fields"
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] = provider_specific_fields
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function_chunk["provider_specific_fields"] = (
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provider_specific_fields
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)
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tool_call_index = parsed_chunk.get("output_index", 0)
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tool_call_chunk = ChatCompletionToolCallChunk(
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@ -1133,9 +1147,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
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# Add provider_specific_fields to function if present
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if provider_specific_fields:
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function_chunk[
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"provider_specific_fields"
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] = provider_specific_fields
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function_chunk["provider_specific_fields"] = (
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provider_specific_fields
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)
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tool_call_index = parsed_chunk.get("output_index", 0)
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tool_call_chunk = ChatCompletionToolCallChunk(
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@ -9,7 +9,9 @@ from unittest.mock import ANY, MagicMock, Mock, patch
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import httpx
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import pytest
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sys.path.insert(0, os.path.abspath("../../..")) # Adds the parent directory to the system-path
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sys.path.insert(
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0, os.path.abspath("../../..")
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) # Adds the parent directory to the system-path
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import litellm
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@ -117,7 +119,9 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_imag
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function_call_output = item
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break
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assert function_call_output is not None, "function_call_output not found in response"
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assert (
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function_call_output is not None
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), "function_call_output not found in response"
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assert function_call_output["call_id"] == "call_abc123"
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# Check that the output is correctly transformed
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@ -127,8 +131,12 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_imag
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image_item = output[0]
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# Should be transformed to Responses API format
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assert image_item["type"] == "input_image", f"Expected type 'input_image', got '{image_item.get('type')}'"
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assert image_item["image_url"] == test_image_base64, "image_url should be a flat string, not a nested object"
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assert (
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image_item["type"] == "input_image"
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), f"Expected type 'input_image', got '{image_item.get('type')}'"
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assert (
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image_item["image_url"] == test_image_base64
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), "image_url should be a flat string, not a nested object"
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assert "detail" in image_item, "detail field should be present"
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print("✓ Tool result with image correctly transformed to Responses API format")
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@ -190,7 +198,9 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_text
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function_call_output = item
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break
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assert function_call_output is not None, "function_call_output not found in response"
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assert (
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function_call_output is not None
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), "function_call_output not found in response"
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assert function_call_output["call_id"] == "call_abc123"
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# Check that the output is correctly transformed to use input_text, not output_text
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@ -200,12 +210,16 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_text
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text_item = output[0]
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# Should be transformed to use input_text for tool results in Responses API format
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assert text_item["type"] == "input_text", (
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f"Expected type 'input_text' for tool result, got '{text_item.get('type')}'"
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)
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assert text_item["text"] == "15 degrees", f"Expected text '15 degrees', got '{text_item.get('text')}'"
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assert (
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text_item["type"] == "input_text"
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), f"Expected type 'input_text' for tool result, got '{text_item.get('type')}'"
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assert (
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text_item["text"] == "15 degrees"
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), f"Expected text '15 degrees', got '{text_item.get('text')}'"
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print("✓ Tool result with text correctly transformed to use input_text for Responses API format")
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print(
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"✓ Tool result with text correctly transformed to use input_text for Responses API format"
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)
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def test_openai_responses_chunk_parser_reasoning_summary():
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@ -214,7 +228,9 @@ def test_openai_responses_chunk_parser_reasoning_summary():
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)
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from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {
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"delta": "**Compar",
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@ -246,7 +262,9 @@ def test_chunk_parser_string_output_text_delta_produces_text():
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)
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from litellm.types.utils import ModelResponseStream
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {"type": "response.output_text.delta", "delta": "literal text"}
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@ -267,7 +285,9 @@ def test_chunk_parser_enum_output_text_delta_produces_text():
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from litellm.types.llms.openai import ResponsesAPIStreamEvents
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from litellm.types.utils import ModelResponseStream
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {"type": ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, "delta": "enum text"}
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@ -288,7 +308,9 @@ def test_chunk_parser_function_call_added_produces_tool_use():
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from litellm.types.llms.openai import ResponsesAPIStreamEvents
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from litellm.types.utils import ModelResponseStream
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {
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"type": ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED,
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@ -373,7 +395,9 @@ Tomorrow will bring its petitions and promises,
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but for now the city breathes slow and wide,
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and I learn to carry this small calm home."""
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output_text = ResponseOutputText(annotations=[], text=poem_text, type="output_text", logprobs=[])
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output_text = ResponseOutputText(
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annotations=[], text=poem_text, type="output_text", logprobs=[]
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)
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output_message = ResponseOutputMessage(
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id="msg_04c8021b8b3188a00068e9ae0b92f4819dac64d85b4abb67ec",
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content=[output_text],
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@ -385,7 +409,9 @@ and I learn to carry this small calm home."""
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# Create usage information
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usage = ResponseAPIUsage(
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input_tokens=16,
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input_tokens_details=InputTokensDetails(audio_tokens=None, cached_tokens=0, text_tokens=None),
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input_tokens_details=InputTokensDetails(
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audio_tokens=None, cached_tokens=0, text_tokens=None
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),
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output_tokens=195,
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output_tokens_details=OutputTokensDetails(reasoning_tokens=0, text_tokens=None),
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total_tokens=211,
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@ -597,7 +623,9 @@ def test_transform_request_single_char_keys_not_matched():
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assert result_correct.get("metadata") == {"user_id": "123"}
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assert result_correct.get("previous_response_id") == "resp_abc"
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print("✓ Single-character keys are not incorrectly matched to metadata/previous_response_id")
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print(
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"✓ Single-character keys are not incorrectly matched to metadata/previous_response_id"
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)
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# =============================================================================
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@ -617,7 +645,9 @@ def test_message_done_does_not_emit_is_finished():
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OpenAiResponsesToChatCompletionStreamIterator,
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)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {
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"type": "response.output_item.done",
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@ -629,9 +659,9 @@ def test_message_done_does_not_emit_is_finished():
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# After the fix, message completion should NOT set finish_reason
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# ModelResponseStream doesn't have is_finished - check finish_reason instead
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assert len(result.choices) > 0, "result should have choices"
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assert result.choices[0].finish_reason is None or result.choices[0].finish_reason == "", (
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"message completion should not emit finish_reason"
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)
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assert (
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result.choices[0].finish_reason is None or result.choices[0].finish_reason == ""
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), "message completion should not emit finish_reason"
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def test_response_completed_emits_is_finished():
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@ -643,7 +673,9 @@ def test_response_completed_emits_is_finished():
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OpenAiResponsesToChatCompletionStreamIterator,
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)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {"type": "response.completed"}
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@ -651,7 +683,9 @@ def test_response_completed_emits_is_finished():
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# response.completed should emit finish_reason='stop'
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assert len(result.choices) > 0, "result should have choices"
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assert result.choices[0].finish_reason == "stop", "response.completed should emit finish_reason='stop'"
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assert (
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result.choices[0].finish_reason == "stop"
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), "response.completed should emit finish_reason='stop'"
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def test_response_completed_with_function_calls_emits_tool_calls_finish_reason():
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@ -670,7 +704,9 @@ def test_response_completed_with_function_calls_emits_tool_calls_finish_reason()
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OpenAiResponsesToChatCompletionStreamIterator,
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)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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# Simulate a response.completed event with function_call in output
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# This matches what Azure/OpenAI sends for gpt-5.1-codex-mini and similar models
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@ -696,9 +732,9 @@ def test_response_completed_with_function_calls_emits_tool_calls_finish_reason()
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# response.completed with function_call should emit finish_reason='tool_calls'
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assert len(result.choices) > 0, "result should have choices"
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assert result.choices[0].finish_reason == "tool_calls", (
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"response.completed with function_call output should emit finish_reason='tool_calls'"
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)
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assert (
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result.choices[0].finish_reason == "tool_calls"
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), "response.completed with function_call output should emit finish_reason='tool_calls'"
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def test_response_completed_with_message_only_emits_stop_finish_reason():
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@ -709,7 +745,9 @@ def test_response_completed_with_message_only_emits_stop_finish_reason():
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OpenAiResponsesToChatCompletionStreamIterator,
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)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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# Simulate a response.completed event with only message output
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chunk = {
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@ -733,10 +771,9 @@ def test_response_completed_with_message_only_emits_stop_finish_reason():
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# response.completed with only message should emit finish_reason='stop'
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assert len(result.choices) > 0, "result should have choices"
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assert result.choices[0].finish_reason == "stop", (
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"response.completed with only message output should emit finish_reason='stop'"
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)
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assert (
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result.choices[0].finish_reason == "stop"
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), "response.completed with only message output should emit finish_reason='stop'"
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def test_response_completed_preserves_usage_with_cached_tokens():
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@ -752,7 +789,9 @@ def test_response_completed_preserves_usage_with_cached_tokens():
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OpenAiResponsesToChatCompletionStreamIterator,
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)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
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)
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chunk = {
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"type": "response.completed",
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@ -781,12 +820,18 @@ def test_response_completed_preserves_usage_with_cached_tokens():
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result = iterator.chunk_parser(chunk)
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assert result.usage is not None, "usage should be set on response.completed chunk"
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assert result.usage.prompt_tokens == 1226, "prompt_tokens should map from input_tokens"
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assert result.usage.completion_tokens == 5, "completion_tokens should map from output_tokens"
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assert result.usage.prompt_tokens_details is not None, "prompt_tokens_details should be set"
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assert result.usage.prompt_tokens_details.cached_tokens == 1024, (
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"cached_tokens should be preserved from input_tokens_details"
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)
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assert (
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result.usage.prompt_tokens == 1226
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), "prompt_tokens should map from input_tokens"
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assert (
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result.usage.completion_tokens == 5
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), "completion_tokens should map from output_tokens"
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assert (
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result.usage.prompt_tokens_details is not None
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), "prompt_tokens_details should be set"
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assert (
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result.usage.prompt_tokens_details.cached_tokens == 1024
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), "cached_tokens should be preserved from input_tokens_details"
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def test_function_call_done_emits_is_finished():
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|
|
@ -800,7 +845,9 @@ def test_function_call_done_emits_is_finished():
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OpenAiResponsesToChatCompletionStreamIterator,
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)
|
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
|
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)
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chunk = {
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"type": "response.output_item.done",
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|
|
@ -820,9 +867,9 @@ def test_function_call_done_emits_is_finished():
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"output_item.done for function_call must not emit finish_reason; "
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"response.completed is responsible for the terminal finish_reason"
|
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)
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assert not result.choices[0].delta.tool_calls, (
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"output_item.done for function_call must not include a duplicate tool_calls delta"
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)
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assert not result.choices[
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0
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].delta.tool_calls, "output_item.done for function_call must not include a duplicate tool_calls delta"
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def test_text_plus_tool_calls_sequence():
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|
|
@ -837,7 +884,9 @@ def test_text_plus_tool_calls_sequence():
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OpenAiResponsesToChatCompletionStreamIterator,
|
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)
|
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|
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iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
|
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iterator = OpenAiResponsesToChatCompletionStreamIterator(
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streaming_response=None, sync_stream=True
|
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)
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# Simulate the sequence from OpenAI Responses API
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chunks = [
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|
|
@ -876,23 +925,28 @@ def test_text_plus_tool_calls_sequence():
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# Check message done (index 2) does NOT have finish_reason set
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message_done_result = results[2]
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assert len(message_done_result.choices) > 0, "message done should have choices"
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assert message_done_result.choices[0].finish_reason is None or message_done_result.choices[0].finish_reason == "", (
|
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"message done should not have finish_reason"
|
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)
|
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assert (
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message_done_result.choices[0].finish_reason is None
|
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or message_done_result.choices[0].finish_reason == ""
|
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), "message done should not have finish_reason"
|
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# Check function_call done (index 5) does NOT have finish_reason set
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# (response.completed is responsible for the terminal finish_reason)
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function_done_result = results[5]
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assert len(function_done_result.choices) > 0, "function_call done should have choices"
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assert function_done_result.choices[0].finish_reason is None, (
|
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"output_item.done for function_call must not emit finish_reason"
|
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)
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assert (
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len(function_done_result.choices) > 0
|
||||
), "function_call done should have choices"
|
||||
assert (
|
||||
function_done_result.choices[0].finish_reason is None
|
||||
), "output_item.done for function_call must not emit finish_reason"
|
||||
|
||||
# Check response.completed (index 6) has finish_reason='stop'
|
||||
# (the mock chunk has no nested 'response' data, so has_function_calls is False → 'stop')
|
||||
completed_result = results[6]
|
||||
assert len(completed_result.choices) > 0, "response.completed should have choices"
|
||||
assert completed_result.choices[0].finish_reason == "stop", "response.completed should have finish_reason='stop'"
|
||||
assert (
|
||||
completed_result.choices[0].finish_reason == "stop"
|
||||
), "response.completed should have finish_reason='stop'"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
|
|
@ -958,7 +1012,9 @@ def test_tool_message_output_uses_input_text_not_output_text():
|
|||
output = function_call_output["output"]
|
||||
assert isinstance(output, list), f"output should be a list, got {type(output)}"
|
||||
assert len(output) == 1
|
||||
assert output[0]["type"] == "input_text", f"Expected input_text, got {output[0].get('type')}"
|
||||
assert (
|
||||
output[0]["type"] == "input_text"
|
||||
), f"Expected input_text, got {output[0].get('type')}"
|
||||
assert output[0]["text"] == '{"temperature": 15, "condition": "sunny"}'
|
||||
|
||||
print("✓ Tool message output correctly uses input_text type")
|
||||
|
|
@ -1144,9 +1200,13 @@ def test_map_reasoning_effort_adds_summary_detailed():
|
|||
|
||||
assert result is not None, f"Result should not be None for effort={effort}"
|
||||
assert result["effort"] == effort, f"Effort should be {effort}"
|
||||
assert "summary" not in result, f"Summary should NOT be present by default for effort={effort}"
|
||||
assert (
|
||||
"summary" not in result
|
||||
), f"Summary should NOT be present by default for effort={effort}"
|
||||
|
||||
print(f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}' (no summary by default)")
|
||||
print(
|
||||
f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}' (no summary by default)"
|
||||
)
|
||||
|
||||
# Test 2: With flag enabled - summary IS added
|
||||
litellm.reasoning_auto_summary = True
|
||||
|
|
@ -1156,9 +1216,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
|
|||
|
||||
assert result is not None, f"Result should not be None for effort={effort}"
|
||||
assert result["effort"] == effort, f"Effort should be {effort}"
|
||||
assert result["summary"] == "detailed", (
|
||||
f"Summary should be 'detailed' when flag is enabled for effort={effort}"
|
||||
)
|
||||
assert (
|
||||
result["summary"] == "detailed"
|
||||
), f"Summary should be 'detailed' when flag is enabled for effort={effort}"
|
||||
|
||||
print(
|
||||
f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}', summary='detailed' (flag enabled)"
|
||||
|
|
@ -1169,7 +1229,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
|
|||
os.environ["LITELLM_REASONING_AUTO_SUMMARY"] = "true"
|
||||
|
||||
result = handler._map_reasoning_effort("high")
|
||||
assert result["summary"] == "detailed", "Summary should be 'detailed' when env var is enabled"
|
||||
assert (
|
||||
result["summary"] == "detailed"
|
||||
), "Summary should be 'detailed' when env var is enabled"
|
||||
print("✓ LITELLM_REASONING_AUTO_SUMMARY env var works correctly")
|
||||
|
||||
# Test 4: Dict input is passed through as-is (no modification)
|
||||
|
|
@ -1188,7 +1250,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
|
|||
assert result_unknown is None
|
||||
print("✓ Unknown reasoning_effort values return None")
|
||||
|
||||
print("✓ All reasoning_effort behaviors work correctly with flag/env var control")
|
||||
print(
|
||||
"✓ All reasoning_effort behaviors work correctly with flag/env var control"
|
||||
)
|
||||
|
||||
finally:
|
||||
# Restore original values
|
||||
|
|
@ -1264,7 +1328,9 @@ def test_transform_response_preserves_annotations():
|
|||
# Create usage information
|
||||
usage = ResponseAPIUsage(
|
||||
input_tokens=10,
|
||||
input_tokens_details=InputTokensDetails(audio_tokens=None, cached_tokens=0, text_tokens=None),
|
||||
input_tokens_details=InputTokensDetails(
|
||||
audio_tokens=None, cached_tokens=0, text_tokens=None
|
||||
),
|
||||
output_tokens=20,
|
||||
output_tokens_details=OutputTokensDetails(reasoning_tokens=0, text_tokens=None),
|
||||
total_tokens=30,
|
||||
|
|
@ -1351,9 +1417,13 @@ def test_transform_response_preserves_annotations():
|
|||
assert choice.message.content == "Here is some information with citations."
|
||||
|
||||
# Check that annotations are preserved
|
||||
assert hasattr(choice.message, "annotations"), "Message should have annotations attribute"
|
||||
assert hasattr(
|
||||
choice.message, "annotations"
|
||||
), "Message should have annotations attribute"
|
||||
assert choice.message.annotations is not None, "Annotations should not be None"
|
||||
assert len(choice.message.annotations) == 2, f"Expected 2 annotations, got {len(choice.message.annotations)}"
|
||||
assert (
|
||||
len(choice.message.annotations) == 2
|
||||
), f"Expected 2 annotations, got {len(choice.message.annotations)}"
|
||||
|
||||
# Verify annotation content
|
||||
annotation1 = choice.message.annotations[0]
|
||||
|
|
@ -1375,7 +1445,9 @@ def test_transform_response_preserves_annotations():
|
|||
assert result.usage.completion_tokens == 20
|
||||
assert result.usage.total_tokens == 30
|
||||
|
||||
print("✓ Annotations from Responses API are correctly preserved in Chat Completions format")
|
||||
print(
|
||||
"✓ Annotations from Responses API are correctly preserved in Chat Completions format"
|
||||
)
|
||||
|
||||
|
||||
def test_apply_patch_tool_call_converted_to_chat_completion_tool_call():
|
||||
|
|
@ -1512,6 +1584,8 @@ def test_apply_patch_tool_call_converted_to_chat_completion_tool_call():
|
|||
assert args["type"] == "create_file"
|
||||
assert args["path"] == "hello.py"
|
||||
assert "print('hello world')" in args["diff"]
|
||||
|
||||
|
||||
def test_multi_tool_call_stream_no_premature_finish():
|
||||
"""
|
||||
Regression test for multi-tool-call streaming bug.
|
||||
|
|
@ -1538,18 +1612,26 @@ def test_multi_tool_call_stream_no_premature_finish():
|
|||
OpenAiResponsesToChatCompletionStreamIterator,
|
||||
)
|
||||
|
||||
iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
|
||||
iterator = OpenAiResponsesToChatCompletionStreamIterator(
|
||||
streaming_response=None, sync_stream=True
|
||||
)
|
||||
|
||||
chunks = [
|
||||
# 0: response created
|
||||
{"type": "response.created", "response": {"id": "resp_001", "status": "in_progress"}},
|
||||
{
|
||||
"type": "response.created",
|
||||
"response": {"id": "resp_001", "status": "in_progress"},
|
||||
},
|
||||
# 1: first tool call added
|
||||
{
|
||||
"type": "response.output_item.added",
|
||||
"item": {"type": "function_call", "name": "read_file", "call_id": "call_1"},
|
||||
},
|
||||
# 2: first tool call arguments delta
|
||||
{"type": "response.function_call_arguments.delta", "delta": '{"path":"/etc/hostname"}'},
|
||||
{
|
||||
"type": "response.function_call_arguments.delta",
|
||||
"delta": '{"path":"/etc/hostname"}',
|
||||
},
|
||||
# 3: first tool call done ← must NOT emit finish_reason
|
||||
{
|
||||
"type": "response.output_item.done",
|
||||
|
|
@ -1608,10 +1690,12 @@ def test_multi_tool_call_stream_no_premature_finish():
|
|||
r = results[done_idx]
|
||||
assert r is not None, f"{label}: chunk_parser must return a result"
|
||||
assert len(r.choices) > 0, f"{label}: result must have choices"
|
||||
assert r.choices[0].finish_reason is None, (
|
||||
f"{label}: output_item.done must not emit finish_reason (stream would terminate prematurely)"
|
||||
)
|
||||
assert not r.choices[0].delta.tool_calls, (
|
||||
assert (
|
||||
r.choices[0].finish_reason is None
|
||||
), f"{label}: output_item.done must not emit finish_reason (stream would terminate prematurely)"
|
||||
assert not r.choices[
|
||||
0
|
||||
].delta.tool_calls, (
|
||||
f"{label}: output_item.done must not include a duplicate tool_calls delta"
|
||||
)
|
||||
|
||||
|
|
@ -1623,12 +1707,12 @@ def test_multi_tool_call_stream_no_premature_finish():
|
|||
r = results[added_idx]
|
||||
if r is not None and r.choices and r.choices[0].delta.tool_calls:
|
||||
tc = r.choices[0].delta.tool_calls[0]
|
||||
assert tc.function.name == expected_name, (
|
||||
f"output_item.added for {expected_name}: tool_call name mismatch"
|
||||
)
|
||||
assert tc.id == expected_call_id, (
|
||||
f"output_item.added for {expected_name}: call_id mismatch"
|
||||
)
|
||||
assert (
|
||||
tc.function.name == expected_name
|
||||
), f"output_item.added for {expected_name}: tool_call name mismatch"
|
||||
assert (
|
||||
tc.id == expected_call_id
|
||||
), f"output_item.added for {expected_name}: call_id mismatch"
|
||||
|
||||
# 3. argument delta events (indices 2 and 5) should carry arguments
|
||||
for delta_idx, expected_args, label in [
|
||||
|
|
@ -1638,17 +1722,17 @@ def test_multi_tool_call_stream_no_premature_finish():
|
|||
r = results[delta_idx]
|
||||
if r is not None and r.choices and r.choices[0].delta.tool_calls:
|
||||
tc = r.choices[0].delta.tool_calls[0]
|
||||
assert tc.function.arguments == expected_args, (
|
||||
f"{label}: argument delta mismatch"
|
||||
)
|
||||
assert (
|
||||
tc.function.arguments == expected_args
|
||||
), f"{label}: argument delta mismatch"
|
||||
|
||||
# 4. Only response.completed (index 7) emits the terminal finish_reason
|
||||
completed_result = results[7]
|
||||
assert completed_result is not None, "response.completed must return a result"
|
||||
assert len(completed_result.choices) > 0, "response.completed must have choices"
|
||||
assert completed_result.choices[0].finish_reason == "tool_calls", (
|
||||
"response.completed with function_call outputs must emit finish_reason='tool_calls'"
|
||||
)
|
||||
assert (
|
||||
completed_result.choices[0].finish_reason == "tool_calls"
|
||||
), "response.completed with function_call outputs must emit finish_reason='tool_calls'"
|
||||
|
||||
# 5. No chunk before the last one should have finish_reason set
|
||||
for idx, r in enumerate(results[:-1]):
|
||||
|
|
@ -1658,7 +1742,9 @@ def test_multi_tool_call_stream_no_premature_finish():
|
|||
f"— only response.completed should terminate the stream"
|
||||
)
|
||||
|
||||
print("✓ Multi-tool-call stream completes without premature finish_reason termination")
|
||||
print(
|
||||
"✓ Multi-tool-call stream completes without premature finish_reason termination"
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
|
|
@ -1790,7 +1876,10 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
|
||||
chunks = [
|
||||
# 0: response.created
|
||||
{"type": "response.created", "response": {"id": "resp_001", "status": "in_progress"}},
|
||||
{
|
||||
"type": "response.created",
|
||||
"response": {"id": "resp_001", "status": "in_progress"},
|
||||
},
|
||||
# 1: call_1 (read_file) added — output_index=0
|
||||
{
|
||||
"type": "response.output_item.added",
|
||||
|
|
@ -1873,7 +1962,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
},
|
||||
]
|
||||
|
||||
iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
|
||||
iterator = OpenAiResponsesToChatCompletionStreamIterator(
|
||||
streaming_response=None, sync_stream=True
|
||||
)
|
||||
results = [iterator.chunk_parser(chunk) for chunk in chunks]
|
||||
|
||||
# 1. output_item.done events (indices 4 and 8) must NOT emit finish_reason
|
||||
|
|
@ -1885,7 +1976,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
f"{label}: output_item.done must not emit finish_reason "
|
||||
f"(would prematurely terminate stream before subsequent tool calls arrive)"
|
||||
)
|
||||
assert not r.choices[0].delta.tool_calls, (
|
||||
assert not r.choices[
|
||||
0
|
||||
].delta.tool_calls, (
|
||||
f"{label}: output_item.done must not emit a duplicate tool_calls delta"
|
||||
)
|
||||
|
||||
|
|
@ -1919,7 +2012,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
for tc in tool_calls:
|
||||
if tc.function and tc.function.arguments:
|
||||
idx = tc.index
|
||||
assembled_args[idx] = assembled_args.get(idx, "") + tc.function.arguments
|
||||
assembled_args[idx] = (
|
||||
assembled_args.get(idx, "") + tc.function.arguments
|
||||
)
|
||||
|
||||
# delta 1 = '{"path":' + delta 2 = '"/etc/foo"}' → '{"path":"/etc/foo"}'
|
||||
assert assembled_args.get(0) == '{"path":"/etc/foo"}', (
|
||||
|
|
@ -1938,16 +2033,16 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
for i, r in enumerate(results)
|
||||
if r is not None and r.choices and r.choices[0].finish_reason
|
||||
]
|
||||
assert len(finish_events) == 1, (
|
||||
f"Expected exactly 1 finish event, got {len(finish_events)}: {finish_events}"
|
||||
)
|
||||
assert (
|
||||
len(finish_events) == 1
|
||||
), f"Expected exactly 1 finish event, got {len(finish_events)}: {finish_events}"
|
||||
assert finish_events[0][0] == len(chunks) - 1, (
|
||||
f"Finish event must be at the last chunk (index {len(chunks) - 1}), "
|
||||
f"but was at index {finish_events[0][0]}"
|
||||
)
|
||||
assert finish_events[0][1] == "tool_calls", (
|
||||
f"Terminal finish_reason must be 'tool_calls', got '{finish_events[0][1]}'"
|
||||
)
|
||||
assert (
|
||||
finish_events[0][1] == "tool_calls"
|
||||
), f"Terminal finish_reason must be 'tool_calls', got '{finish_events[0][1]}'"
|
||||
|
||||
# 5. Parallel tool calls have distinct indices matching output_index (0 and 1)
|
||||
# Collect indices from output_item.added chunks only (they carry the call id)
|
||||
|
|
@ -1958,16 +2053,19 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
|
|||
for tc in r.choices[0].delta.tool_calls
|
||||
if tc.id # output_item.added chunks carry the id; argument deltas do not
|
||||
]
|
||||
assert set(added_tool_call_indices) == {0, 1}, (
|
||||
f"Parallel tool calls must have distinct indices {{0, 1}}, got: {set(added_tool_call_indices)}"
|
||||
)
|
||||
assert set(added_tool_call_indices) == {
|
||||
0,
|
||||
1,
|
||||
}, f"Parallel tool calls must have distinct indices {{0, 1}}, got: {set(added_tool_call_indices)}"
|
||||
|
||||
print("✓ Parallel tool calls with split argument deltas stream correctly end-to-end")
|
||||
print(
|
||||
"✓ Parallel tool calls with split argument deltas stream correctly end-to-end"
|
||||
)
|
||||
|
||||
|
||||
def test_map_optional_params_preserves_reasoning_summary():
|
||||
"""Test that reasoning_effort dict with summary field is preserved.
|
||||
|
||||
|
||||
Regression test for: User reported that summary field was being dropped
|
||||
when routing to Responses API. The dict format should be fully preserved.
|
||||
"""
|
||||
|
|
@ -1992,6 +2090,97 @@ def test_map_optional_params_preserves_reasoning_summary():
|
|||
|
||||
# Verify reasoning_effort dict with summary was fully preserved
|
||||
assert "reasoning" in responses_api_request
|
||||
assert responses_api_request["reasoning"] == {"effort": "high", "summary": "detailed"}
|
||||
assert responses_api_request["reasoning"] == {
|
||||
"effort": "high",
|
||||
"summary": "detailed",
|
||||
}
|
||||
assert responses_api_request["reasoning"]["effort"] == "high"
|
||||
assert responses_api_request["reasoning"]["summary"] == "detailed"
|
||||
|
||||
|
||||
def test_convert_chat_completion_file_type_to_input_file():
|
||||
"""
|
||||
Test that Chat Completion content with type 'file' is correctly mapped
|
||||
to Responses API 'input_file' format, not stringified as 'input_text'.
|
||||
|
||||
Regression test for https://github.com/BerriAI/litellm/issues/23588
|
||||
"""
|
||||
from litellm.completion_extras.litellm_responses_transformation.transformation import (
|
||||
LiteLLMResponsesTransformationHandler,
|
||||
)
|
||||
|
||||
handler = LiteLLMResponsesTransformationHandler()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "What is in this PDF?"},
|
||||
{
|
||||
"type": "file",
|
||||
"file": {
|
||||
"file_data": "data:application/pdf;base64,JVBERi0xLjQK",
|
||||
"filename": "test.pdf",
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
input_items, instructions = (
|
||||
handler.convert_chat_completion_messages_to_responses_api(messages)
|
||||
)
|
||||
|
||||
assert len(input_items) == 1
|
||||
msg = input_items[0]
|
||||
assert msg["type"] == "message"
|
||||
assert msg["role"] == "user"
|
||||
|
||||
content = msg["content"]
|
||||
assert len(content) == 2
|
||||
|
||||
# First item should be the text
|
||||
assert content[0]["type"] == "input_text"
|
||||
assert content[0]["text"] == "What is in this PDF?"
|
||||
|
||||
# Second item should be input_file, NOT input_text with stringified dict
|
||||
assert content[1]["type"] == "input_file"
|
||||
assert content[1]["file_data"] == "data:application/pdf;base64,JVBERi0xLjQK"
|
||||
assert content[1]["filename"] == "test.pdf"
|
||||
# Ensure it does NOT have the nested 'file' key
|
||||
assert "file" not in content[1]
|
||||
|
||||
|
||||
def test_convert_chat_completion_file_type_with_file_id():
|
||||
"""
|
||||
Test that Chat Completion content with type 'file' using file_id is correctly mapped.
|
||||
"""
|
||||
from litellm.completion_extras.litellm_responses_transformation.transformation import (
|
||||
LiteLLMResponsesTransformationHandler,
|
||||
)
|
||||
|
||||
handler = LiteLLMResponsesTransformationHandler()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Summarize this file."},
|
||||
{
|
||||
"type": "file",
|
||||
"file": {
|
||||
"file_id": "file-abc123",
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
input_items, instructions = (
|
||||
handler.convert_chat_completion_messages_to_responses_api(messages)
|
||||
)
|
||||
|
||||
content = input_items[0]["content"]
|
||||
assert content[1]["type"] == "input_file"
|
||||
assert content[1]["file_id"] == "file-abc123"
|
||||
assert "file_data" not in content[1]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue