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| """ | |
| Appel du LLM génératif — DeepSeek-V4.1-Flash — via le routeur "Inference | |
| Providers" de Hugging Face (https://router.huggingface.co), qui proxy | |
| automatiquement vers un provider tiers (Novita à ce jour) tout en | |
| n'exigeant qu'un seul jeton HF_TOKEN pour l'authentification et la | |
| facturation. Aucune autre clé d'API n'est nécessaire. | |
| """ | |
| from __future__ import annotations | |
| import time | |
| from dataclasses import dataclass | |
| from functools import lru_cache | |
| from core.generation.prompts import build_messages, no_context_message | |
| from core.retrieval.hybrid_search import RetrievalResult | |
| from config import settings | |
| class GenerationResult: | |
| answer: str | |
| used_fallback: bool | |
| latency_seconds: float | |
| sources: list[dict] | |
| def _get_client(): | |
| from huggingface_hub import InferenceClient | |
| if not settings.hf_token: | |
| raise RuntimeError("HF_TOKEN manquant : impossible d'appeler le LLM.") | |
| return InferenceClient(api_key=settings.hf_token) | |
| def generate_answer( | |
| question: str, | |
| retrieval: RetrievalResult, | |
| target_language: str, | |
| ) -> GenerationResult: | |
| start = time.perf_counter() | |
| if not retrieval.is_sufficient: | |
| return GenerationResult( | |
| answer=no_context_message(target_language), | |
| used_fallback=True, | |
| latency_seconds=time.perf_counter() - start, | |
| sources=[], | |
| ) | |
| messages = build_messages(question, retrieval.hits, target_language) | |
| client = _get_client() | |
| completion = client.chat.completions.create( | |
| model=settings.llm_model, | |
| messages=messages, | |
| temperature=settings.llm_temperature, | |
| max_tokens=settings.llm_max_tokens, | |
| ) | |
| answer = completion.choices[0].message.content | |
| sources = [ | |
| { | |
| "file": hit.source_file, | |
| "language": hit.language, | |
| "page": hit.page, | |
| "score": round(hit.score, 3), | |
| "title": hit.title, | |
| } | |
| for hit in retrieval.hits | |
| ] | |
| return GenerationResult( | |
| answer=answer, | |
| used_fallback=False, | |
| latency_seconds=time.perf_counter() - start, | |
| sources=sources, | |
| ) | |