5a31f52310
Services: Admin-Lehrer, Backend-Lehrer, Studio v2, Website, Klausur-Service, School-Service, Voice-Service, Geo-Service, BreakPilot Drive, Agent-Core Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
"""
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Chat Completions Route - OpenAI-kompatible API.
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"""
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import logging
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import json
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from typing import AsyncIterator
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from fastapi import APIRouter, HTTPException, Depends
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from fastapi.responses import StreamingResponse
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from ..models.chat import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatMessage,
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ModelListResponse,
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)
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from ..services.inference import get_inference_service, InferenceService
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from ..services.playbook_service import get_playbook_service, PlaybookService
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from ..middleware.auth import verify_api_key
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=["LLM"])
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def get_services():
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"""Dependency für Services."""
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return get_inference_service(), get_playbook_service()
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@router.post("/chat/completions", response_model=ChatCompletionResponse)
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async def chat_completions(
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request: ChatCompletionRequest,
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_: str = Depends(verify_api_key),
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):
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"""
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OpenAI-kompatible Chat Completions.
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Unterstützt:
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- Streaming (stream=true)
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- Playbook-basierte System Prompts (metadata.playbook_id)
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- Multiple Models (breakpilot-teacher-8b, claude-3-5-sonnet, etc.)
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"""
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inference_service, playbook_service = get_services()
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# Playbook System Prompt injizieren
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if request.metadata and request.metadata.playbook_id:
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playbook = playbook_service.get_playbook(request.metadata.playbook_id)
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if playbook:
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# System Prompt an den Anfang der Messages einfügen
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system_msg = ChatMessage(role="system", content=playbook.system_prompt)
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# Prüfen ob bereits ein System Prompt existiert
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has_system = any(m.role == "system" for m in request.messages)
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if not has_system:
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request.messages.insert(0, system_msg)
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else:
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# Playbook Prompt vor bestehenden System Prompt setzen
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for i, msg in enumerate(request.messages):
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if msg.role == "system":
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msg.content = f"{playbook.system_prompt}\n\n{msg.content}"
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break
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try:
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if request.stream:
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return StreamingResponse(
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stream_response(request, inference_service),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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else:
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response = await inference_service.complete(request)
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return response
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except ValueError as e:
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logger.error(f"Chat completion error: {e}")
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.exception(f"Chat completion failed: {e}")
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raise HTTPException(status_code=500, detail="Internal server error")
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async def stream_response(
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request: ChatCompletionRequest,
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inference_service: InferenceService,
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) -> AsyncIterator[str]:
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"""Generator für SSE Streaming."""
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try:
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async for chunk in inference_service.stream(request):
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data = chunk.model_dump_json()
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yield f"data: {data}\n\n"
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yield "data: [DONE]\n\n"
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except Exception as e:
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logger.exception(f"Streaming error: {e}")
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error_data = json.dumps({"error": str(e)})
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yield f"data: {error_data}\n\n"
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@router.get("/models", response_model=ModelListResponse)
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async def list_models(
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_: str = Depends(verify_api_key),
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):
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"""
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Liste verfügbarer Modelle.
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Gibt alle konfigurierten Modelle zurück, die aktuell verfügbar sind.
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"""
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inference_service = get_inference_service()
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return await inference_service.list_models()
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