Add vLLM message normalization for OpenAI content format compatibility
- Normalize 'developer' role to 'system' (vLLM doesn't support developer role) - Flatten array content to string for text-only messages - Preserve mixed content (text + images) as array - Add comprehensive unit tests for normalization logic Fixes HTTP 422 errors when clients send OpenAI multi-content format
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@@ -27,6 +27,47 @@ logger = logging.getLogger(__name__)
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router = APIRouter()
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def normalize_messages_for_vllm(messages: list) -> list:
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"""Normalize OpenAI message format for vLLM compatibility.
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vLLM is stricter than OpenAI API and requires:
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1. Message content as string (not array of content parts)
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2. Role must be system/user/assistant/function/tool (not "developer")
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Args:
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messages: List of message dicts
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Returns:
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Normalized list of messages
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"""
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normalized = []
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for msg in messages:
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normalized_msg = msg.copy()
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# Normalize "developer" role to "system"
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if normalized_msg.get("role") == "developer":
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normalized_msg["role"] = "system"
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logger.debug("Normalized 'developer' role to 'system'")
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# Normalize array content to string for text-only messages
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content = normalized_msg.get("content")
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if isinstance(content, list):
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# Check if all parts are text-only
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if all(isinstance(p, dict) and p.get("type") == "text" for p in content):
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# Flatten to concatenated string
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normalized_msg["content"] = "\n".join(p.get("text", "") for p in content)
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logger.debug(f"Normalized array content to string: {len(content)} parts")
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else:
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# Has image_url or other non-text types - keep as is
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# vLLM may reject this, but we preserve the original format
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logger.warning("Message contains non-text content parts, keeping array format")
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normalized.append(normalized_msg)
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return normalized
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@router.post(
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"/v1/chat/completions",
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response_model=Union[ChatCompletionResponse, ErrorResponse],
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@@ -51,8 +92,14 @@ async def create_chat_completion(
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watsonx_model = settings.map_model(request.model)
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logger.info(f"Chat completion request: {request.model} -> {watsonx_model}")
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# Transform messages
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watsonx_messages = transform_messages_to_watsonx(request.messages)
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# Normalize messages for vLLM compatibility (handles array content and developer role)
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normalized_messages = normalize_messages_for_vllm([msg.model_dump() for msg in request.messages])
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# Transform normalized messages to watsonx format
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# Convert back to ChatMessage objects for the transformer
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from app.models.openai_models import ChatMessage
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normalized_chat_messages = [ChatMessage(**msg) for msg in normalized_messages]
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watsonx_messages = transform_messages_to_watsonx(normalized_chat_messages)
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# Transform tools if present
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watsonx_tools = transform_tools_to_watsonx(request.tools)
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95
tests/test_message_normalization.py
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95
tests/test_message_normalization.py
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@@ -0,0 +1,95 @@
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"""Tests for message normalization for vLLM compatibility."""
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import pytest
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from app.routers.chat import normalize_messages_for_vllm
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def test_normalize_developer_role():
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"""Test that 'developer' role is normalized to 'system'."""
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messages = [
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{"role": "developer", "content": "You are helpful."},
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{"role": "user", "content": "Hello"}
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]
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normalized = normalize_messages_for_vllm(messages)
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assert normalized[0]["role"] == "system"
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assert normalized[1]["role"] == "user"
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def test_normalize_array_content_text_only():
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"""Test that array content with only text parts is flattened to string."""
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Hello"},
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{"type": "text", "text": "World"}
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]
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}
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]
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normalized = normalize_messages_for_vllm(messages)
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assert isinstance(normalized[0]["content"], str)
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assert normalized[0]["content"] == "Hello\nWorld"
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def test_normalize_string_content_unchanged():
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"""Test that string content remains unchanged."""
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messages = [
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{"role": "user", "content": "Hello world"}
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]
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normalized = normalize_messages_for_vllm(messages)
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assert normalized[0]["content"] == "Hello world"
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def test_normalize_mixed_content_preserved():
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"""Test that mixed content (text + image) is preserved as array."""
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
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]
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}
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]
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normalized = normalize_messages_for_vllm(messages)
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# Should keep array format for mixed content
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assert isinstance(normalized[0]["content"], list)
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assert len(normalized[0]["content"]) == 2
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def test_normalize_multiple_messages():
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"""Test normalization of multiple messages with different formats."""
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messages = [
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{"role": "developer", "content": "You are helpful."},
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{"role": "user", "content": [{"type": "text", "text": "Hello"}]},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "user", "content": "How are you?"}
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]
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normalized = normalize_messages_for_vllm(messages)
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assert normalized[0]["role"] == "system"
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assert normalized[1]["content"] == "Hello"
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assert normalized[2]["content"] == "Hi there!"
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assert normalized[3]["content"] == "How are you?"
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def test_normalize_empty_content():
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"""Test handling of empty or None content."""
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messages = [
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{"role": "user", "content": None},
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{"role": "user", "content": ""}
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]
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normalized = normalize_messages_for_vllm(messages)
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assert normalized[0]["content"] is None
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assert normalized[1]["content"] == ""
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