Add Chat Completions wrapper with reasoning conversion
Implements Phase 5: OpenAI Chat Completions compatibility layer Features: - Wraps Responses API for single source of truth - Automatically enables reasoning generation - Converts reasoning items to <think> tags for Open WebUI - Maintains OpenAI-compatible chat completion format - Supports both streaming and non-streaming modes - Pipeline prefix preservation for model names - System message handling Architecture: - Service layer calls Responses API internally - Streams word-by-word for smooth UX - Reasoning displayed in thought bubbles (Open WebUI) - Main response shown separately from thinking Error Handling: - Enhanced exception types (RateLimitError, ContextLengthError) - OpenAI-compatible error format - Graceful error propagation from Responses API Testing: - 6 unit tests for chat router functionality - 6 unit tests for streaming wrapper behavior - Total: 12 tests with comprehensive coverage 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Claude
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@@ -1,12 +1,15 @@
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"""
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Chat completion service.
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Currently returns mock responses with lorem ipsum.
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TODO: Integrate with Ollama/PydanticAI in future.
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Wrapper around Responses API that converts to Chat Completions format.
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Embeds reasoning in <think> tags for Open WebUI compatibility.
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"""
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import asyncio
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import time
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import uuid
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from typing import AsyncGenerator
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from src.agents.registry import ModelRegistry
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from src.chat import constants
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from src.chat.schemas import (
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ChatCompletionChunk,
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@@ -20,29 +23,67 @@ from src.chat.schemas import (
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)
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# Mock lorem ipsum response
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MOCK_RESPONSE = (
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"Lorem ipsum dolor sit amet, consectetur adipiscing elit. "
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"Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. "
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"Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris."
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)
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async def create_chat_completion(
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request: ChatCompletionRequest,
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) -> ChatCompletionResponse:
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"""
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Create chat completion (mock implementation).
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Create chat completion by wrapping Responses API.
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Converts Responses API output to Chat Completions format with
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reasoning embedded in <think> tags for Open WebUI.
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Args:
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request: Chat completion request
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Returns:
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Mock chat completion response with lorem ipsum
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Chat completion response with reasoning as <think> tags
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"""
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completion_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
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created_at = int(time.time())
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# Strip pipeline prefix if present
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model_id = request.model
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if "." in model_id:
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model_id = model_id.split(".", 1)[1]
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# Get agent and generate response
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agent = ModelRegistry.get_agent(model_id)
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# Convert Chat messages to Responses format
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input_messages = [
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{"role": msg.role, "content": msg.content}
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for msg in request.messages
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]
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# Collect output items from agent (with reasoning enabled)
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output_items = []
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async for item in agent.generate_response(
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messages=input_messages,
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reasoning={"effort": "medium", "summary": "auto"}, # Enable reasoning
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temperature=request.temperature or 1.0,
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max_tokens=request.max_tokens,
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stop=request.stop if isinstance(request.stop, list) else ([request.stop] if request.stop else None),
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):
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output_items.append(item)
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# Build content with <think> tags
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content_parts = []
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# Add reasoning as <think> blocks
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for item in output_items:
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if item.type == "reasoning":
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reasoning_text = "\n".join(item.data.get("summary", []))
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content_parts.append(f"<think>\n{reasoning_text}\n</think>\n\n")
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elif item.type == "message":
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content_parts.append(item.data["content"][0]["text"])
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content = "".join(content_parts)
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# Calculate token usage (approximate)
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prompt_text = " ".join(m.content for m in request.messages)
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prompt_tokens = len(prompt_text) // 4
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completion_tokens = len(content) // 4
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return ChatCompletionResponse(
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id=completion_id,
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object=constants.CHAT_COMPLETION_OBJECT,
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@@ -53,16 +94,15 @@ async def create_chat_completion(
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index=0,
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message=ChatMessage(
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role=constants.ROLE_ASSISTANT,
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content=MOCK_RESPONSE,
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content=content,
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),
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finish_reason=constants.FINISH_REASON_STOP,
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)
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],
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usage=ChatCompletionUsage(
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prompt_tokens=len(" ".join(m.content for m in request.messages).split()),
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completion_tokens=len(MOCK_RESPONSE.split()),
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total_tokens=len(" ".join(m.content for m in request.messages).split())
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+ len(MOCK_RESPONSE.split()),
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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),
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)
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@@ -71,20 +111,33 @@ async def create_chat_completion_stream(
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request: ChatCompletionRequest,
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) -> AsyncGenerator[ChatCompletionChunk, None]:
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"""
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Create streaming chat completion (mock implementation).
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Create streaming chat completion by wrapping Responses API.
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Streams reasoning in <think> tags followed by message content.
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Args:
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request: Chat completion request with stream=True
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Yields:
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Mock chat completion chunks with lorem ipsum
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Chat completion chunks with reasoning as <think> tags
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"""
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completion_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
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created_at = int(time.time())
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# Split response into words for streaming simulation
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words = MOCK_RESPONSE.split()
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# Strip pipeline prefix if present
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model_id = request.model
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if "." in model_id:
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model_id = model_id.split(".", 1)[1]
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# Get agent
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agent = ModelRegistry.get_agent(model_id)
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# Convert Chat messages to Responses format
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input_messages = [
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{"role": msg.role, "content": msg.content}
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for msg in request.messages
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]
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# First chunk with role
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yield ChatCompletionChunk(
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id=completion_id,
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@@ -99,23 +152,86 @@ async def create_chat_completion_stream(
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)
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],
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)
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# Stream words
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for word in words:
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yield ChatCompletionChunk(
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id=completion_id,
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object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
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created=created_at,
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model=request.model,
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choices=[
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ChatCompletionChunkChoice(
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index=0,
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delta=ChatCompletionChunkDelta(content=f"{word} "),
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finish_reason=None,
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# Stream from agent with reasoning enabled
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in_reasoning = False
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async for item in agent.generate_response(
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messages=input_messages,
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reasoning={"effort": "medium", "summary": "auto"}, # Enable reasoning
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temperature=request.temperature or 1.0,
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max_tokens=request.max_tokens,
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stop=request.stop if isinstance(request.stop, list) else ([request.stop] if request.stop else None),
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):
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if item.type == "reasoning":
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# Start <think> block
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if not in_reasoning:
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yield ChatCompletionChunk(
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id=completion_id,
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object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
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created=created_at,
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model=request.model,
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choices=[
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ChatCompletionChunkChoice(
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index=0,
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delta=ChatCompletionChunkDelta(content="<think>\n"),
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finish_reason=None,
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)
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],
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)
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],
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)
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in_reasoning = True
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# Stream reasoning summary steps
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for step in item.data.get("summary", []):
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yield ChatCompletionChunk(
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id=completion_id,
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object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
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created=created_at,
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model=request.model,
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choices=[
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ChatCompletionChunkChoice(
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index=0,
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delta=ChatCompletionChunkDelta(content=f"{step}\n"),
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finish_reason=None,
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)
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],
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)
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await asyncio.sleep(0.05) # Simulate typing
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# Close <think> block
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yield ChatCompletionChunk(
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id=completion_id,
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object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
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created=created_at,
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model=request.model,
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choices=[
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ChatCompletionChunkChoice(
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index=0,
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delta=ChatCompletionChunkDelta(content="</think>\n\n"),
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finish_reason=None,
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)
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],
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)
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in_reasoning = False
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elif item.type == "message":
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# Stream message content word by word
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text = item.data["content"][0]["text"]
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for word in text.split():
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yield ChatCompletionChunk(
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id=completion_id,
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object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
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created=created_at,
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model=request.model,
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choices=[
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ChatCompletionChunkChoice(
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index=0,
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delta=ChatCompletionChunkDelta(content=f"{word} "),
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finish_reason=None,
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)
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],
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)
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await asyncio.sleep(0.05) # Simulate typing
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# Final chunk with finish_reason
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yield ChatCompletionChunk(
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id=completion_id,
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+22
-1
@@ -47,6 +47,27 @@ class ModelNotFoundError(AppException):
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class ValidationError(AppException):
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"""Raised for validation errors."""
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def __init__(self, message: str, details: dict[str, Any] | None = None):
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super().__init__(message=message, status_code=422, details=details)
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class RateLimitError(AppException):
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"""Raised when rate limit is exceeded."""
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def __init__(self, message: str = "Rate limit exceeded"):
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super().__init__(message=message, status_code=429)
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class ContextLengthError(AppException):
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"""Raised when context length exceeds model limits."""
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def __init__(self, message: str = "Context length exceeded"):
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super().__init__(message=message, status_code=400)
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class APIError(AppException):
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"""Generic API error."""
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def __init__(self, message: str, status_code: int = 500):
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super().__init__(message=message, status_code=status_code)
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