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feat: add convert_output_to_messages for OR-aligned message building

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This commit is contained in:
Tim Baek
2026-01-22 19:54:05 -05:00
parent 46cf40ec82
commit d2c695eb11
3 changed files with 207 additions and 2 deletions
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118
backend/open_webui/utils/middleware.py
View File

@@ -92,6 +92,7 @@ from open_webui.utils.misc import (
prepend_to_first_user_message_content,
convert_logit_bias_input_to_json,
get_content_from_message,
convert_output_to_messages,
)
from open_webui.utils.tools import (
get_tools,
@@ -1399,6 +1400,30 @@ async def convert_url_images_to_base64(form_data):
return form_data
def process_messages_with_output(messages: list[dict]) -> list[dict]:
"""
Process messages with OR-aligned output items for LLM consumption.
For assistant messages with 'output' field, produces properly formatted
OpenAI-style messages (tool_calls + tool results). Strips 'output' before LLM.
"""
processed = []
for message in messages:
if message.get("role") == "assistant" and message.get("output"):
# Use output items for clean OpenAI-format messages
output_messages = convert_output_to_messages(message["output"])
if output_messages:
processed.extend(output_messages)
continue
# Strip 'output' field before adding (LLM shouldn't see it)
clean_message = {k: v for k, v in message.items() if k != "output"}
processed.append(clean_message)
return processed
async def process_chat_payload(request, form_data, user, metadata, model):
# Pipeline Inlet -> Filter Inlet -> Chat Memory -> Chat Web Search -> Chat Image Generation
# -> Chat Code Interpreter (Form Data Update) -> (Default) Chat Tools Function Calling
@@ -1407,6 +1432,9 @@ async def process_chat_payload(request, form_data, user, metadata, model):
form_data = apply_params_to_form_data(form_data, model)
log.debug(f"form_data: {form_data}")
# Process messages with OR-aligned output items for clean LLM messages
form_data["messages"] = process_messages_with_output(form_data.get("messages", []))
system_message = get_system_message(form_data.get("messages", []))
if system_message: # Chat Controls/User Settings
try:
@@ -2517,6 +2545,86 @@ async def process_chat_response(
return messages
def convert_content_blocks_to_output(content_blocks):
"""
Convert content_blocks to Open Responses-aligned output items.
See: https://openresponses.org/specification
"""
output_items = []
item_counter = 0
def next_id(prefix):
nonlocal item_counter
item_counter += 1
return f"{prefix}_{item_counter}"
for block in content_blocks:
block_type = block.get("type", "")
if block_type == "text":
text_content = block.get("content", "").strip()
if text_content:
output_items.append({
"type": "message",
"id": next_id("msg"),
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": text_content}],
})
elif block_type == "tool_calls":
tool_calls = block.get("content", [])
results = block.get("results", [])
# Emit function_call items
for tool_call in tool_calls:
call_id = tool_call.get("id", "")
func = tool_call.get("function", {})
output_items.append({
"type": "function_call",
"id": next_id("fc"),
"call_id": call_id,
"name": func.get("name", ""),
"arguments": func.get("arguments", "{}"),
"status": "completed" if results else "in_progress",
})
# Emit function_call_output items
for result in results:
output_items.append({
"type": "function_call_output",
"id": next_id("fco"),
"call_id": result.get("tool_call_id", ""),
"output": [{"type": "input_text", "text": result.get("content", "")}],
"status": "completed",
})
elif block_type == "reasoning":
reasoning_content = block.get("content", "").strip()
duration = block.get("duration")
output_items.append({
"type": "reasoning",
"id": next_id("r"),
"status": "completed" if duration is not None else "in_progress",
"content": [{"type": "output_text", "text": reasoning_content}] if reasoning_content else None,
"summary": None,
})
elif block_type == "code_interpreter":
code = block.get("content", "")
output_val = block.get("output")
attrs = block.get("attributes", {})
output_items.append({
"type": "open_webui:code_interpreter",
"id": next_id("ci"),
"status": "completed" if output_val is not None else "in_progress",
"lang": attrs.get("lang", ""),
"code": code,
"output": output_val,
})
return output_items
def tag_content_handler(content_type, tags, content, content_blocks):
end_flag = False
@@ -3132,6 +3240,9 @@ async def process_chat_response(
"content": serialize_content_blocks(
content_blocks
),
"output": convert_content_blocks_to_output(
content_blocks
),
},
)
else:
@@ -3221,6 +3332,7 @@ async def process_chat_response(
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
}
)
@@ -3400,6 +3512,7 @@ async def process_chat_response(
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
}
)
@@ -3446,6 +3559,7 @@ async def process_chat_response(
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
}
)
@@ -3577,6 +3691,7 @@ async def process_chat_response(
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
}
)
@@ -3616,6 +3731,7 @@ async def process_chat_response(
data = {
"done": True,
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
"title": title,
}
@@ -3626,6 +3742,7 @@ async def process_chat_response(
metadata["message_id"],
{
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
)
@@ -3664,6 +3781,7 @@ async def process_chat_response(
metadata["message_id"],
{
"content": serialize_content_blocks(content_blocks),
"output": convert_content_blocks_to_output(content_blocks),
},
)

80
backend/open_webui/utils/misc.py
View File

@@ -128,6 +128,86 @@ def get_content_from_message(message: dict) -> Optional[str]:
return None
def convert_output_to_messages(output: list) -> list[dict]:
"""
Convert OR-aligned output items to OpenAI-format messages for LLM consumption.
This is the inverse of convert_content_blocks_to_output() in middleware.py.
"""
if not output or not isinstance(output, list):
return []
messages = []
pending_tool_calls = []
pending_content = []
for item in output:
item_type = item.get("type", "")
if item_type == "message":
# Extract text from output_text content parts
content_parts = item.get("content", [])
text = ""
for part in content_parts:
if part.get("type") == "output_text":
text += part.get("text", "")
if text:
pending_content.append(text)
elif item_type == "function_call":
# Collect tool calls to batch into assistant message
pending_tool_calls.append({
"id": item.get("call_id", ""),
"type": "function",
"function": {
"name": item.get("name", ""),
"arguments": item.get("arguments", "{}"),
}
})
elif item_type == "function_call_output":
# Flush any pending content/tool_calls before adding tool result
if pending_content or pending_tool_calls:
messages.append({
"role": "assistant",
"content": "\n".join(pending_content) if pending_content else "",
**({"tool_calls": pending_tool_calls} if pending_tool_calls else {}),
})
pending_content = []
pending_tool_calls = []
# Extract text from output content parts
output_parts = item.get("output", [])
content = ""
for part in output_parts:
if part.get("type") == "input_text":
content += part.get("text", "")
messages.append({
"role": "tool",
"tool_call_id": item.get("call_id", ""),
"content": content,
})
elif item_type == "reasoning":
# Skip reasoning blocks for LLM messages
pass
elif item_type.startswith("open_webui:"):
# Skip extension types
pass
# Flush remaining content/tool_calls
if pending_content or pending_tool_calls:
messages.append({
"role": "assistant",
"content": "\n".join(pending_content) if pending_content else "",
**({"tool_calls": pending_tool_calls} if pending_tool_calls else {}),
})
return messages
def get_last_user_message(messages: list[dict]) -> Optional[str]:
message = get_last_user_message_item(messages)
if message is None:

11
src/lib/components/chat/Chat.svelte
View File

@@ -1403,7 +1403,12 @@
};
const chatCompletionEventHandler = async (data, message, chatId) => {
const { id, done, choices, content, sources, selected_model_id, error, usage } = data;
const { id, done, choices, content, output, sources, selected_model_id, error, usage } = data;
// Store raw OR-aligned output items from backend
if (output) {
message.output = output;
}
if (error) {
await handleOpenAIError(error, message);
@@ -1899,7 +1904,9 @@
: undefined,
..._messages.map((message) => ({
...message,
content: processDetails(message.content)
content: processDetails(message.content),
// Include output for temp chats (backend will use it and strip before LLM)
...(message.output ? { output: message.output } : {})
}))
].filter((message) => message);