Refactor SRP : decoupage de main.py (Brain) en routers et du SettingsComponent (web)
Brain : main.py (1496 l.) reduit a l'assemblage (~95 l.) ; un router par responsabilite (generation, chat, tables, imports, notebooks, settings, models), factories DI dans api/deps.py, DTOs chat + mapping anti-corruption separes, auto-pull embeddings deplace en infrastructure. Chemins HTTP inchanges. Web : SettingsComponent (729 l.) recentre sur le formulaire (~330 l.) ; sous-composants standalone updates-section (MAJ + licence Patreon + switch canal) et ollama-model-manager (liste/pull/suppression de modeles). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
365
brain/app/api/routers/models.py
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365
brain/app/api/routers/models.py
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"""Endpoints de catalogue de modèles (Ollama, OpenRouter, Mistral, Gemini, 1min.ai).
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Proxifie les APIs des providers pour que l'UI propose des listes de modèles ;
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repli statique quand l'API est injoignable ou la clé absente (pas de 500 à l'UI).
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"""
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import json
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from typing import Annotated, AsyncIterator
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import httpx
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from fastapi import APIRouter, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from app.core.config import Settings, get_settings
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router = APIRouter()
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@router.get("/models/ollama")
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async def list_ollama_models(
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settings: Annotated[Settings, Depends(get_settings)],
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) -> dict[str, list[str]]:
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"""Liste les modeles disponibles sur le serveur Ollama configure.
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Retourne une liste vide si Ollama est injoignable — l'UI affichera un
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message plutot qu'une 500.
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"""
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url = f"{settings.ollama_base_url}/api/tags"
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try:
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async with httpx.AsyncClient(timeout=5) as client:
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response = await client.get(url)
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response.raise_for_status()
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data = response.json()
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except httpx.HTTPError:
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return {"models": []}
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models = [m.get("name", "") for m in data.get("models", []) if m.get("name")]
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return {"models": sorted(models)}
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class OllamaModelInfoDTO(BaseModel):
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"""Info utile extraite de /api/show pour un modele Ollama donne.
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`context_length` = fenetre de contexte max supportee par le modele
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(extraite des metadonnees GGUF). 0 si inconnue. Le frontend s'en sert
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pour borner le slider de num_ctx dans les Parametres.
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"""
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context_length: int = 0
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@router.post("/models/ollama/info", response_model=OllamaModelInfoDTO)
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async def get_ollama_model_info(
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body: dict[str, str],
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settings: Annotated[Settings, Depends(get_settings)],
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) -> OllamaModelInfoDTO:
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"""Retourne les metadonnees d'un modele Ollama via /api/show.
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On passe par POST (et pas GET /models/ollama/{name}) parce que les noms
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Ollama contiennent souvent un `:` (ex: `gemma3:e2b`) qui se segmente
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mal dans une URL — le body JSON evite le probleme d'escaping.
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Le champ qui nous interesse est `model_info["<arch>.context_length"]`
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(ex: `gemma3.context_length: 131072`). L'arch varie selon le modele, on
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scanne donc tous les champs finissant par `.context_length`.
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"""
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name = (body.get("name") or "").strip()
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if not name:
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raise HTTPException(status_code=400, detail="name requis")
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url = f"{settings.ollama_base_url}/api/show"
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try:
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async with httpx.AsyncClient(timeout=5) as client:
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response = await client.post(url, json={"model": name})
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response.raise_for_status()
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data = response.json()
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except httpx.HTTPError:
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return OllamaModelInfoDTO(context_length=0)
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model_info = data.get("model_info") or {}
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for key, value in model_info.items():
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if key.endswith(".context_length") and isinstance(value, int):
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return OllamaModelInfoDTO(context_length=value)
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return OllamaModelInfoDTO(context_length=0)
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@router.post("/models/ollama/pull")
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async def pull_ollama_model(
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body: dict[str, str],
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settings: Annotated[Settings, Depends(get_settings)],
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) -> StreamingResponse:
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"""Telecharge un modele depuis Ollama et streame la progression.
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Proxifie l'endpoint `/api/pull` d'Ollama qui renvoie du JSON ligne par
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ligne (NDJSON) avec le statut de chaque etape : manifest, layers,
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digest, success. On reemet ce flux tel quel au client (le front
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parsera les lignes et affichera une barre de progression).
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Le timeout est intentionnellement tres long (60 min) car certains
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modeles font 30+ Go.
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"""
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name = (body.get("name") or "").strip()
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if not name:
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raise HTTPException(status_code=400, detail="name requis")
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url = f"{settings.ollama_base_url}/api/pull"
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async def stream() -> AsyncIterator[bytes]:
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# On utilise un timeout long pour la lecture (60 min) mais court pour
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# la connexion (10s) — si Ollama n'est pas joignable, on echoue vite.
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timeout = httpx.Timeout(connect=10, read=3600, write=10, pool=10)
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try:
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async with httpx.AsyncClient(timeout=timeout) as client:
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async with client.stream("POST", url, json={"model": name, "stream": True}) as r:
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if r.status_code != 200:
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# Ollama renvoie un message JSON d'erreur. On le passe
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# tel quel au client en preservant le code HTTP.
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body_text = await r.aread()
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yield body_text
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return
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async for chunk in r.aiter_bytes():
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yield chunk
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except httpx.HTTPError as e:
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# Erreur reseau : on emet une ligne JSON d'erreur compatible
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# avec le format NDJSON d'Ollama.
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err = json.dumps({"error": f"Connexion a Ollama impossible : {e}"}) + "\n"
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yield err.encode("utf-8")
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# application/x-ndjson : un objet JSON par ligne, pas de wrapping SSE.
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# C'est le format natif d'Ollama, le front le parsera ligne par ligne.
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return StreamingResponse(stream(), media_type="application/x-ndjson")
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@router.delete("/models/ollama/{name:path}")
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async def delete_ollama_model(
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name: str,
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settings: Annotated[Settings, Depends(get_settings)],
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) -> dict[str, str]:
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"""Supprime un modele du serveur Ollama.
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Le `:path` dans le pattern autorise les `:` du nom (ex: `gemma4:e4b`)
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sans avoir besoin de URL-encoder cote client.
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"""
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if not name.strip():
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raise HTTPException(status_code=400, detail="name requis")
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url = f"{settings.ollama_base_url}/api/delete"
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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response = await client.request("DELETE", url, json={"model": name})
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if response.status_code == 404:
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raise HTTPException(status_code=404, detail=f"Modele '{name}' introuvable")
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response.raise_for_status()
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except httpx.HTTPError as e:
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raise HTTPException(status_code=502, detail=f"Ollama injoignable : {e}")
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return {"status": "deleted", "name": name}
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@router.get("/models/openrouter")
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async def list_openrouter_models() -> dict[str, list[dict[str, object]]]:
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"""Catalogue DYNAMIQUE des modeles OpenRouter (API publique, sans cle).
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Renvoie {models: [{id, name, context_length, free}]}, trie gratuits d'abord
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puis contexte decroissant. `free` = id finissant par ':free' OU prix nul.
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"""
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try:
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async with httpx.AsyncClient(timeout=20) as client:
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response = await client.get("https://openrouter.ai/api/v1/models")
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response.raise_for_status()
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data = response.json()
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except httpx.HTTPError as exc:
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raise HTTPException(status_code=502, detail=f"OpenRouter injoignable : {exc}")
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def _is_zero(value: object) -> bool:
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try:
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return float(value) == 0.0 # type: ignore[arg-type]
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except (TypeError, ValueError):
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return False
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models: list[dict[str, object]] = []
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for m in data.get("data", []) or []:
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mid = str(m.get("id") or "")
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if not mid:
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continue
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pricing = m.get("pricing") or {}
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is_free = mid.endswith(":free") or (
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_is_zero(pricing.get("prompt")) and _is_zero(pricing.get("completion"))
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)
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try:
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ctx = int(m.get("context_length") or 0)
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except (TypeError, ValueError):
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ctx = 0
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models.append({
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"id": mid,
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"name": str(m.get("name") or mid),
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"context_length": ctx,
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"free": is_free,
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})
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models.sort(key=lambda x: (not x["free"], -int(x["context_length"]))) # type: ignore[index]
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return {"models": models}
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# Repli statique si la cle Mistral n'est pas (encore) configuree ou si l'API est
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# injoignable — l'utilisateur peut quand meme choisir un modele. Liste curee
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# (juin 2026) ; pour l'extraction de PDF, prefere `large` (fidele, 128k) ou `small`.
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_MISTRAL_FALLBACK_MODELS = [
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"mistral-large-latest",
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"mistral-medium-latest",
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"mistral-small-latest",
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"open-mistral-nemo",
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"ministral-8b-latest",
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"ministral-3b-latest",
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"magistral-medium-latest",
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"magistral-small-latest",
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"pixtral-large-latest",
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"codestral-latest",
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]
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@router.get("/models/mistral")
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async def list_mistral_models(
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settings: Annotated[Settings, Depends(get_settings)],
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) -> dict[str, list[dict[str, object]]]:
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"""Catalogue des modeles Mistral. Dynamique si une cle est configuree
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(GET /v1/models, qui requiert l'auth), sinon repli statique.
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Renvoie {models: [{id}]} (tous accessibles sur le tier gratuit Experiment)."""
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key = settings.mistral_api_key
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if not key:
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return {"models": [{"id": m} for m in _MISTRAL_FALLBACK_MODELS]}
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try:
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async with httpx.AsyncClient(timeout=20) as client:
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response = await client.get(
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"https://api.mistral.ai/v1/models",
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headers={"Authorization": f"Bearer {key}"},
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)
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response.raise_for_status()
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data = response.json()
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except httpx.HTTPError:
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# Cle invalide / API down : on ne casse pas l'UI, on propose le repli.
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return {"models": [{"id": m} for m in _MISTRAL_FALLBACK_MODELS]}
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ids = sorted({str(m.get("id")) for m in data.get("data", []) or [] if m.get("id")})
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if not ids:
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ids = _MISTRAL_FALLBACK_MODELS
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return {"models": [{"id": i} for i in ids]}
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# Repli statique Gemini (juin 2026). Pour l'extraction, prefere un Flash a grand
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# contexte ; `gemini-2.0-flash` a le quota gratuit le plus genereux.
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_GEMINI_FALLBACK_MODELS = [
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"gemini-2.0-flash",
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"gemini-2.0-flash-lite",
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"gemini-2.5-flash",
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"gemini-2.5-flash-lite",
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"gemini-2.5-pro",
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"gemini-1.5-flash",
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"gemini-1.5-pro",
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]
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@router.get("/models/gemini")
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async def list_gemini_models(
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settings: Annotated[Settings, Depends(get_settings)],
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) -> dict[str, list[dict[str, object]]]:
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"""Catalogue des modeles Gemini. Dynamique si une cle est configuree (endpoint
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OpenAI-compatible /openai/models), sinon repli statique. Renvoie {models:[{id}]}."""
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key = settings.gemini_api_key
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if not key:
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return {"models": [{"id": m} for m in _GEMINI_FALLBACK_MODELS]}
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try:
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async with httpx.AsyncClient(timeout=20) as client:
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response = await client.get(
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"https://generativelanguage.googleapis.com/v1beta/openai/models",
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headers={"Authorization": f"Bearer {key}"},
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)
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response.raise_for_status()
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data = response.json()
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except httpx.HTTPError:
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return {"models": [{"id": m} for m in _GEMINI_FALLBACK_MODELS]}
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# Les ids peuvent arriver prefixes "models/" → on nettoie pour que la valeur
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# selectionnee soit directement utilisable dans l'appel chat. On garde les
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# modeles "gemini-*" (hors embeddings/aqa) pour ne pas noyer la liste.
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ids: set[str] = set()
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for m in data.get("data", []) or []:
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mid = str(m.get("id") or "")
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if mid.startswith("models/"):
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mid = mid[len("models/"):]
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if mid.startswith("gemini-"):
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ids.add(mid)
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clean = sorted(ids) if ids else _GEMINI_FALLBACK_MODELS
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return {"models": [{"id": i} for i in clean]}
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@router.get("/models/onemin")
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def list_onemin_models() -> dict[str, list[dict[str, object]]]:
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"""Catalogue statique des modeles 1min.ai, groupes par fournisseur.
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Liste construite par probing direct de l'endpoint chat-with-ai avec
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une vraie cle API (avril 2026) : chaque ID renvoie 200, les IDs
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absents renvoient 400 UNSUPPORTED_MODEL.
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Nota : les IDs Anthropic utilisent la nomenclature propre a 1min.ai
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(`claude-<family>-<version>`), pas la convention officielle Anthropic.
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"""
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return {
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"groups": [
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{
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"provider": "Anthropic",
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"models": ["claude-opus-4-6", "claude-sonnet-4-6"],
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},
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{
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"provider": "OpenAI",
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"models": [
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"gpt-5",
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"gpt-5-mini",
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"gpt-5-nano",
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"gpt-4.1",
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"gpt-4.1-mini",
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"gpt-4.1-nano",
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"gpt-4o",
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"gpt-4o-mini",
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"gpt-4-turbo",
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"gpt-3.5-turbo",
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"o3",
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"o3-pro",
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"o3-mini",
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"o4-mini",
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],
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},
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{
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"provider": "Google",
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"models": ["gemini-2.5-pro", "gemini-2.5-flash"],
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},
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{
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"provider": "Mistral",
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"models": [
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"mistral-large-latest",
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"mistral-medium-latest",
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"mistral-small-latest",
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"open-mistral-nemo",
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],
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},
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{
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"provider": "DeepSeek",
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"models": ["deepseek-chat", "deepseek-reasoner"],
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},
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{
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"provider": "xAI",
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"models": ["grok-3", "grok-3-mini"],
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},
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{
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"provider": "Meta",
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"models": [
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"meta/meta-llama-3.1-405b-instruct",
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"meta/meta-llama-3-70b-instruct",
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],
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},
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{
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"provider": "Alibaba",
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"models": ["qwen-plus", "qwen3-max"],
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},
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{
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"provider": "Perplexity",
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"models": ["sonar", "sonar-pro"],
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},
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]
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}
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