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fix(interactions): map step and turn input to Responses API roles and content types
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mateo-berri
parent
d86336a7c6
commit
3e94f7d71e
2 changed files with 146 additions and 85 deletions
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@ -2,10 +2,11 @@
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Transformation utilities for bridging Interactions API to Responses API.
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This module handles transforming between:
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- Interactions API format (Google's format with Turn[], system_instruction, etc.)
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- Interactions API format (Google's format with Step[]/Turn[], system_instruction, etc.)
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- Responses API format (OpenAI's format with input[], instructions, etc.)
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"""
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from types import MappingProxyType
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from typing import Any, Final, cast
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from litellm.types.interactions import (
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@ -19,6 +20,8 @@ from litellm.types.llms.openai import (
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ResponsesAPIResponse,
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)
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_STEP_TYPE_ROLES: Final = MappingProxyType({"user_input": "user", "model_output": "assistant"})
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class LiteLLMResponsesInteractionsConfig:
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"""Configuration class for transforming between Interactions API and Responses API."""
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@ -91,112 +94,98 @@ class LiteLLMResponsesInteractionsConfig:
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Interactions API input can be:
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- string: "Hello"
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- Turn[]: [{"role": "user", "content": [...]}]
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- Content object
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- Step[]: [{"type": "user_input", "content": [...]}, {"type": "model_output", "content": [...]}]
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- Turn[] (legacy): [{"role": "user", "content": [...]}]
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- Content | Content[]: one user message worth of content parts
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Responses API input is:
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- string: "Hello"
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- Message[]: [{"role": "user", "content": [...]}]
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- Message[]: [{"role": "user", "content": [{"type": "input_text", ...}]}]
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"""
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if isinstance(input, str):
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# ResponseInputParam accepts str
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return cast(ResponseInputParam, input)
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if isinstance(input, list):
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# Turn[] format - convert to Responses API Message[] format
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messages: Final = []
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for turn in input:
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if isinstance(turn, dict):
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role = turn.get("role", "user")
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content = turn.get("content", [])
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# Transform content array
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transformed_content = LiteLLMResponsesInteractionsConfig._transform_content_array(content)
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messages.append(
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{
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"role": role,
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"content": transformed_content,
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}
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)
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elif isinstance(turn, Turn):
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# Pydantic model
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role = turn.role if hasattr(turn, "role") else "user"
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content = turn.content if hasattr(turn, "content") else []
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# Ensure content is a list for _transform_content_array
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# Cast to List[Any] to handle various content types
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if isinstance(content, list):
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content_list: list[Any] = list(content)
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elif content is not None:
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content_list = [content]
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else:
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content_list = []
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transformed_content = LiteLLMResponsesInteractionsConfig._transform_content_array(content_list)
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messages.append(
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{
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"role": role,
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"content": transformed_content,
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}
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)
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return cast(ResponseInputParam, messages)
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# Single content object - wrap in message
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if isinstance(input, dict):
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if any(LiteLLMResponsesInteractionsConfig._is_history_item(item) for item in input):
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return cast(
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ResponseInputParam,
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[
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LiteLLMResponsesInteractionsConfig._transform_history_item(item)
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for item in input
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if LiteLLMResponsesInteractionsConfig._is_history_item(item)
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],
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)
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return cast(
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ResponseInputParam,
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[
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{
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"role": "user",
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"content": LiteLLMResponsesInteractionsConfig._transform_content_array(
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input.get("content", []) if isinstance(input.get("content"), list) else [input]
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),
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"content": LiteLLMResponsesInteractionsConfig._transform_content_array(list(input), "user"),
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}
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],
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)
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if isinstance(input, dict):
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raw_content: Final = input.get("content")
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content_items: Final = raw_content if isinstance(raw_content, list) else [input]
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return cast(
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ResponseInputParam,
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[
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{
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"role": "user",
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"content": LiteLLMResponsesInteractionsConfig._transform_content_array(content_items, "user"),
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}
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],
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)
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# Fallback: convert to string
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return cast(ResponseInputParam, str(input))
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@staticmethod
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def _transform_content_array(content: list[Any]) -> list[dict[str, Any]]:
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"""Transform Interactions API content array to Responses API format."""
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if not isinstance(content, list):
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# Single content item - wrap in array
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content = [content]
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def _is_history_item(item: Any) -> bool:
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if isinstance(item, Turn):
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return True
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return isinstance(item, dict) and ("role" in item or item.get("type") in _STEP_TYPE_ROLES)
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transformed: Final[list[dict[str, Any]]] = []
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for item in content:
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if isinstance(item, dict):
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# Already in dict format, pass through
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transformed.append(item)
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elif isinstance(item, str):
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# Plain string - wrap in text format
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transformed.append({"type": "text", "text": item})
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else:
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# Pydantic model or other - convert to dict
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if hasattr(item, "model_dump"):
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dumped = item.model_dump()
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if isinstance(dumped, dict):
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transformed.append(dumped)
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else:
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# Fallback: wrap in text format
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transformed.append({"type": "text", "text": str(dumped)})
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elif hasattr(item, "dict"):
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dumped = item.dict()
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if isinstance(dumped, dict):
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transformed.append(dumped)
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else:
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# Fallback: wrap in text format
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transformed.append({"type": "text", "text": str(dumped)})
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else:
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# Fallback: wrap in text format
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transformed.append({"type": "text", "text": str(item)})
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@staticmethod
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def _transform_history_item(item: "Turn | dict[str, Any]") -> dict[str, Any]:
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raw: Final = item.model_dump(exclude_none=True) if isinstance(item, Turn) else item
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role: Final = LiteLLMResponsesInteractionsConfig._responses_role(raw)
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raw_content: Final = raw.get("content")
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content_items: Final = (
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raw_content if isinstance(raw_content, list) else [] if raw_content is None else [raw_content]
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)
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return {
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"role": role,
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"content": LiteLLMResponsesInteractionsConfig._transform_content_array(content_items, role),
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}
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return transformed
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@staticmethod
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def _responses_role(item: dict[str, Any]) -> str:
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step_role: Final = _STEP_TYPE_ROLES.get(str(item.get("type", "")))
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if step_role is not None:
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return step_role
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raw_role: Final = str(item.get("role") or "user")
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return "assistant" if raw_role == "model" else raw_role
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@staticmethod
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def _transform_content_array(content: list[Any], role: str) -> list[dict[str, Any]]:
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"""Transform Interactions API content parts to Responses API parts for the given role."""
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return [LiteLLMResponsesInteractionsConfig._transform_content_item(item, role) for item in content]
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@staticmethod
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def _transform_content_item(item: Any, role: str) -> dict[str, Any]:
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text_type: Final = "output_text" if role == "assistant" else "input_text"
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if isinstance(item, str):
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return {"type": text_type, "text": item}
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if isinstance(item, dict):
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if item.get("type") == "text":
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return {"type": text_type, "text": str(item.get("text", ""))}
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return item
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if hasattr(item, "model_dump"):
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dumped: Final = item.model_dump(exclude_none=True)
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if isinstance(dumped, dict):
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return LiteLLMResponsesInteractionsConfig._transform_content_item(dumped, role)
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return {"type": text_type, "text": str(item)}
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@staticmethod
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def transform_responses_response_to_interactions_response(
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@ -7,6 +7,10 @@ the litellm_responses bridge provider, which calls litellm.responses() internall
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import os
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from litellm.interactions.litellm_responses_transformation.transformation import (
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LiteLLMResponsesInteractionsConfig,
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)
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from litellm.types.interactions import Turn
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from tests.test_litellm.interactions.base_interactions_test import (
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BaseInteractionsTest,
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)
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@ -26,3 +30,71 @@ class TestLiteLLMResponsesBridge(BaseInteractionsTest):
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def get_api_key(self) -> str:
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"""Return the OpenAI API key from environment."""
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return os.getenv("OPENAI_API_KEY", "")
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class TestBridgeInputTransformation:
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"""Regression tests for translating Interactions input into Responses API input.
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The bridge used to pass Google content parts through raw ({"type": "text"}),
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which the Responses API rejects with a 400, and it dropped the role encoded
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in step types and in the legacy "model" turn role.
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"""
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def test_step_input_maps_roles_and_content_types(self):
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input(
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[
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{"type": "user_input", "content": [{"type": "text", "text": "I like apples."}]},
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{"type": "model_output", "content": [{"type": "text", "text": "I like oranges."}]},
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{"type": "user_input", "content": [{"type": "text", "text": "What did you say?"}]},
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]
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)
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assert transformed == [
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{"role": "user", "content": [{"type": "input_text", "text": "I like apples."}]},
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{"role": "assistant", "content": [{"type": "output_text", "text": "I like oranges."}]},
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{"role": "user", "content": [{"type": "input_text", "text": "What did you say?"}]},
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]
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def test_legacy_turn_input_maps_model_role_to_assistant(self):
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input(
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[
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{"role": "user", "content": [{"type": "text", "text": "I like apples."}]},
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{"role": "model", "content": [{"type": "text", "text": "I like oranges."}]},
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]
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)
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assert transformed == [
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{"role": "user", "content": [{"type": "input_text", "text": "I like apples."}]},
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{"role": "assistant", "content": [{"type": "output_text", "text": "I like oranges."}]},
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]
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def test_turn_pydantic_model_with_string_content(self):
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input(
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[Turn(role="model", content="I like oranges.")]
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)
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assert transformed == [
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{"role": "assistant", "content": [{"type": "output_text", "text": "I like oranges."}]}
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]
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def test_string_input_passes_through(self):
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input("Hello")
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assert transformed == "Hello"
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def test_content_list_input_becomes_single_user_message(self):
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input(
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[{"type": "text", "text": "Hello"}, "world"]
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)
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assert transformed == [
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{
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"role": "user",
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"content": [
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{"type": "input_text", "text": "Hello"},
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{"type": "input_text", "text": "world"},
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],
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}
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]
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def test_non_text_content_passes_through_unchanged(self):
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image_part = {"type": "image", "data": "base64data", "mime_type": "image/png"}
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transformed = LiteLLMResponsesInteractionsConfig._transform_interactions_input_to_responses_input(
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[{"type": "user_input", "content": [image_part]}]
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)
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assert transformed == [{"role": "user", "content": [image_part]}]
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