refac
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Tim Baek
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@@ -128,22 +128,40 @@ def get_content_from_message(message: dict) -> Optional[str]:
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return None
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def convert_output_to_messages(output: list) -> list[dict]:
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def convert_output_to_messages(output: list, raw: bool = False) -> list[dict]:
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"""
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Convert OR-aligned output items to OpenAI-format messages for LLM consumption.
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This is the inverse of convert_content_blocks_to_output() in middleware.py.
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Convert OR-aligned output items to OpenAI Chat Completion-format messages.
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This reconstructs the full conversation from the stored Responses API-native
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output items, including assistant messages with tool_calls arrays and tool
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role messages.
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Args:
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output: List of OR-aligned output items (Responses API format).
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raw: If True, include reasoning blocks (with original tags) and code
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interpreter blocks for LLM re-processing follow-ups.
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"""
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if not output or not isinstance(output, list):
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return []
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messages = []
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pending_tool_calls = []
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pending_content = []
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def flush_pending():
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nonlocal pending_content, pending_tool_calls
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if pending_content or pending_tool_calls:
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messages.append({
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"role": "assistant",
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"content": "\n".join(pending_content) if pending_content else "",
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**({"tool_calls": pending_tool_calls} if pending_tool_calls else {}),
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})
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pending_content = []
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pending_tool_calls = []
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for item in output:
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item_type = item.get("type", "")
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if item_type == "message":
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# Extract text from output_text content parts
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content_parts = item.get("content", [])
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@@ -153,58 +171,86 @@ def convert_output_to_messages(output: list) -> list[dict]:
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text += part.get("text", "")
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if text:
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pending_content.append(text)
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elif item_type == "function_call":
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# Collect tool calls to batch into assistant message
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arguments = item.get("arguments", "{}")
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# Ensure arguments is always a JSON string
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if not isinstance(arguments, str):
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arguments = json.dumps(arguments)
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pending_tool_calls.append({
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"id": item.get("call_id", ""),
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"type": "function",
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"function": {
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"name": item.get("name", ""),
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"arguments": item.get("arguments", "{}"),
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"arguments": arguments,
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}
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})
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elif item_type == "function_call_output":
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# Flush any pending content/tool_calls before adding tool result
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if pending_content or pending_tool_calls:
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messages.append({
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"role": "assistant",
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"content": "\n".join(pending_content) if pending_content else "",
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**({"tool_calls": pending_tool_calls} if pending_tool_calls else {}),
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})
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pending_content = []
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pending_tool_calls = []
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flush_pending()
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# Extract text from output content parts
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output_parts = item.get("output", [])
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content = ""
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for part in output_parts:
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if part.get("type") == "input_text":
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content += part.get("text", "")
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messages.append({
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"role": "tool",
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"tool_call_id": item.get("call_id", ""),
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"content": content,
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})
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elif item_type == "reasoning":
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# Skip reasoning blocks for LLM messages
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pass
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if raw:
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# Include reasoning with original tags for LLM re-processing
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reasoning_text = ""
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source_list = item.get("summary", []) or item.get("content", [])
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for part in source_list:
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if part.get("type") == "output_text":
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reasoning_text += part.get("text", "")
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elif "text" in part:
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reasoning_text += part.get("text", "")
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if reasoning_text:
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start_tag = item.get("start_tag", "<think>")
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end_tag = item.get("end_tag", "</think>")
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pending_content.append(
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f"{start_tag}{reasoning_text}{end_tag}"
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)
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# else: skip reasoning blocks for normal LLM messages
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elif item_type == "open_webui:code_interpreter":
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if raw:
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# Include code interpreter content for LLM re-processing
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code = item.get("code", "")
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code_output = item.get("output", "")
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if code:
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lang = item.get("lang", "python")
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pending_content.append(f"```{lang}\n{code}\n```")
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if code_output:
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if isinstance(code_output, dict):
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stdout = code_output.get("stdout", "")
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result = code_output.get("result", "")
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output_text = stdout or result
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else:
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output_text = str(code_output)
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if output_text:
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pending_content.append(f"Output:\n{output_text}")
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# else: skip extension types
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elif item_type.startswith("open_webui:"):
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# Skip extension types
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# Skip other extension types
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pass
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# Flush remaining content/tool_calls
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if pending_content or pending_tool_calls:
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messages.append({
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"role": "assistant",
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"content": "\n".join(pending_content) if pending_content else "",
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**({"tool_calls": pending_tool_calls} if pending_tool_calls else {}),
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})
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flush_pending()
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return messages
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