120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
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# -*- coding: utf-8 -*-
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"""API-Routen für KI-Services (Transkription, KG-Erstellung, Chat)."""
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form
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from pydantic import BaseModel
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from ..core.models import Employee
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from .auth import get_current_user
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from ..ai import service as ai_service
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router = APIRouter(prefix="/ai", tags=["KI-Service"])
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# ── Transkription ──────────────────────────────
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class TranscribeResponse(BaseModel):
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text: str
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tokens_estimated: int
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@router.post("/transcribe", response_model=TranscribeResponse)
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async def transcribe(
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file: UploadFile = File(...),
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language: str = Form("de"),
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_user: Employee = Depends(get_current_user),
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):
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if not file.filename or not file.filename.lower().endswith(".wav"):
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raise HTTPException(400, "Nur WAV-Dateien erlaubt")
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wav_bytes = await file.read()
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if len(wav_bytes) > 50 * 1024 * 1024:
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raise HTTPException(413, "Datei zu gross (max 50 MB)")
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try:
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result = ai_service.transcribe_audio(wav_bytes, language)
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return TranscribeResponse(**result)
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except RuntimeError as e:
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raise HTTPException(503, str(e))
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except Exception as e:
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raise HTTPException(500, f"Transkription fehlgeschlagen: {e}")
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# ── KG-Erstellung ─────────────────────────────
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class SummarizeRequest(BaseModel):
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transcript: str
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system_prompt: str = ""
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model: str = "gpt-4o"
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class SummarizeResponse(BaseModel):
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text: str
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tokens_used: int
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model: str
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@router.post("/summarize", response_model=SummarizeResponse)
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def summarize(
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data: SummarizeRequest,
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_user: Employee = Depends(get_current_user),
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):
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try:
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result = ai_service.summarize_transcript(
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data.transcript, data.system_prompt, data.model
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)
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return SummarizeResponse(**result)
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except RuntimeError as e:
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raise HTTPException(503, str(e))
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except Exception as e:
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raise HTTPException(500, f"Zusammenfassung fehlgeschlagen: {e}")
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# ── KG-Merge ──────────────────────────────────
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class MergeRequest(BaseModel):
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existing_kg: str
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full_transcript: str
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system_prompt: str = ""
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model: str = "gpt-4o"
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@router.post("/merge", response_model=SummarizeResponse)
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def merge_kg(
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data: MergeRequest,
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_user: Employee = Depends(get_current_user),
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):
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try:
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result = ai_service.merge_kg(
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data.existing_kg, data.full_transcript,
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data.system_prompt, data.model,
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)
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return SummarizeResponse(**result)
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except RuntimeError as e:
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raise HTTPException(503, str(e))
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except Exception as e:
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raise HTTPException(500, f"Merge fehlgeschlagen: {e}")
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# ── Generischer Chat ──────────────────────────
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class ChatRequest(BaseModel):
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messages: list[dict]
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model: str = "gpt-4o"
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@router.post("/chat", response_model=SummarizeResponse)
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def chat_completion(
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data: ChatRequest,
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_user: Employee = Depends(get_current_user),
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):
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try:
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result = ai_service.chat_completion(data.messages, data.model)
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return SummarizeResponse(**result)
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except RuntimeError as e:
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raise HTTPException(503, str(e))
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except Exception as e:
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raise HTTPException(500, f"Chat fehlgeschlagen: {e}")
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