refactor: Sprint 3b — remove dead code, non-functional provider, unused fields

- Remove non-functional VertexAPIKeyProvider (always returned is_configured=False)
- Remove VERTEX_API_KEY enum value and all references
- Remove ModelConfig.available_models field (always passed as [])
- Remove _MISTRAL_VISION_MODELS backward-compat alias
- Remove vertex_api_key from config, client_factory, model_registry
- Clean up related tests

https://claude.ai/code/session_012NCh8yLxMXkRmBYQgHCTik

backend/app/config.py

@@ -49,7 +49,6 @@ class Settings(BaseSettings):
     # providers sont disponibles selon les clés présentes. Pas de AI_PROVIDER
     # global : le provider est choisi par corpus depuis l'interface.
     google_ai_studio_api_key: str | None = None
-    vertex_api_key: str | None = None
     vertex_service_account_json: str | None = None
     mistral_api_key: str | None = None
 

backend/app/schemas/model_config.py

@@ -4,7 +4,6 @@ Schémas Pydantic pour la configuration et la découverte des modèles IA.
 # 1. stdlib
 from datetime import datetime
 from enum import Enum
-from typing import Any
 
 # 2. third-party
 from pydantic import BaseModel, ConfigDict, Field
@@ -12,7 +11,6 @@ from pydantic import BaseModel, ConfigDict, Field
 
 class ProviderType(str, Enum):
     GOOGLE_AI_STUDIO = "google_ai_studio"
-    VERTEX_API_KEY = "vertex_api_key"
     VERTEX_SERVICE_ACCOUNT = "vertex_service_account"
     MISTRAL = "mistral"
 
@@ -39,4 +37,3 @@ class ModelConfig(BaseModel):
     provider: ProviderType
     supports_vision: bool
     last_fetched_at: datetime
-    available_models: list[dict[str, Any]]  # cache sérialisé des ModelInfo

backend/app/services/ai/client_factory.py

@@ -28,11 +28,10 @@ def build_client(provider_type: ProviderType) -> genai.Client:
 
     Lit les variables d'environnement nécessaires selon le provider :
     - GOOGLE_AI_STUDIO  → GOOGLE_AI_STUDIO_API_KEY
-    - VERTEX_API_KEY    → VERTEX_API_KEY
     - VERTEX_SA         → VERTEX_SERVICE_ACCOUNT_JSON
 
     Args:
-        provider_type: type de provider (GOOGLE_AI_STUDIO, VERTEX_API_KEY,
+        provider_type: type de provider (GOOGLE_AI_STUDIO,
                        VERTEX_SERVICE_ACCOUNT).
 
     Returns:
@@ -51,19 +50,6 @@ def build_client(provider_type: ProviderType) -> genai.Client:
         logger.debug("Client Google AI Studio créé")
         return genai.Client(api_key=api_key)
 
-    if provider_type == ProviderType.VERTEX_API_KEY:
-        api_key = os.environ.get("VERTEX_API_KEY")
-        if not api_key:
-            raise RuntimeError(
-                "Variable d'environnement manquante : VERTEX_API_KEY"
-            )
-        logger.debug("Client Vertex AI Express (clé API) créé")
-        # vertexai=True route vers aiplatform.googleapis.com (Vertex AI).
-        # Sans vertexai=True, le SDK route vers generativelanguage.googleapis.com
-        # (Gemini Developer API) qui rejette les clés Vertex Express avec 403.
-        # project/location sont omis : mutually exclusive avec api_key dans le SDK.
-        return genai.Client(vertexai=True, api_key=api_key)
-
     if provider_type == ProviderType.VERTEX_SERVICE_ACCOUNT:
         sa_json_str = os.environ.get("VERTEX_SERVICE_ACCOUNT_JSON")
         if not sa_json_str:

backend/app/services/ai/model_registry.py

@@ -17,7 +17,6 @@ logger = logging.getLogger(__name__)
 # Noms lisibles par provider (pour l'interface)
 _PROVIDER_DISPLAY_NAMES: dict[ProviderType, str] = {
     ProviderType.GOOGLE_AI_STUDIO: "Google AI Studio",
-    ProviderType.VERTEX_API_KEY: "Vertex AI (clé API)",
     ProviderType.VERTEX_SERVICE_ACCOUNT: "Vertex AI (compte de service)",
     ProviderType.MISTRAL: "Mistral AI",
 }
@@ -34,12 +33,10 @@ def _build_providers() -> list[AIProvider]:
 
     from app.services.ai.provider_google_ai import GoogleAIProvider
     from app.services.ai.provider_mistral import MistralProvider
-    from app.services.ai.provider_vertex_key import VertexAPIKeyProvider
     from app.services.ai.provider_vertex_sa import VertexServiceAccountProvider
 
     _providers_cache = [
         GoogleAIProvider(),
-        VertexAPIKeyProvider(),
         VertexServiceAccountProvider(),
         MistralProvider(),
     ]
@@ -171,5 +168,4 @@ def build_model_config(corpus_id: str, selected_model_id: str) -> ModelConfig:
         provider=selected.provider,
         supports_vision=selected.supports_vision,
         last_fetched_at=datetime.now(tz=timezone.utc),
-        available_models=[m.model_dump() for m in models],
     )

backend/app/services/ai/provider_mistral.py

@@ -63,10 +63,6 @@ _MISTRAL_FALLBACK_MODELS: list[ModelInfo] = [
     ),
 ]
 
-# Alias backward-compat (utilisé dans certains tests)
-_MISTRAL_VISION_MODELS = _MISTRAL_FALLBACK_MODELS
-
-
 def _is_ocr_model(model_id: str) -> bool:
     """Retourne True si le modèle utilise l'endpoint OCR dédié (pas chat completions)."""
     return "ocr" in model_id.lower()

backend/app/services/ai/provider_vertex_key.py

@@ -1,61 +0,0 @@
-"""
-Provider Vertex AI — authentification via clé API Express Vertex (VERTEX_API_KEY).
-
-ÉTAT : NON FONCTIONNEL — aiplatform.googleapis.com n'accepte pas les clés API.
-
-Diagnostic :
-  - Sans vertexai=True → generativelanguage.googleapis.com → 403 (clé Vertex rejetée)
-  - Avec vertexai=True  → aiplatform.googleapis.com → 401 UNAUTHENTICATED
-    "API keys are not supported by this API. Expected OAuth2 access token."
-
-Cause : Vertex AI (aiplatform) n'accepte que OAuth2 / service account / ADC.
-Les clés API (format AQ.Ab...) ne sont pas prises en charge par cette API.
-
-Alternatives fonctionnelles :
-  1. Google AI Studio : GOOGLE_AI_STUDIO_API_KEY (clé AIza...) → fonctionne
-  2. Vertex AI Service Account : VERTEX_SERVICE_ACCOUNT_JSON → fonctionne
-
-Ce provider est conservé pour la cohérence de l'interface mais is_configured()
-retourne toujours False afin d'éviter des appels réseau voués à l'échec.
-"""
-# 1. stdlib
-import logging
-import os
-
-# 3. local
-from app.schemas.model_config import ModelInfo, ProviderType
-from app.services.ai.base import AIProvider
-
-logger = logging.getLogger(__name__)
-
-_ENV_KEY = "VERTEX_API_KEY"
-
-_UNAVAILABLE_MSG = (
-    "VERTEX_API_KEY définie mais aiplatform.googleapis.com n'accepte pas les "
-    "clés API (OAuth2 requis). Utilisez GOOGLE_AI_STUDIO_API_KEY pour le "
-    "Gemini Developer API, ou VERTEX_SERVICE_ACCOUNT_JSON pour Vertex AI."
-)
-
-
-class VertexAPIKeyProvider(AIProvider):
-    """Provider Vertex AI via clé API Express — NON FONCTIONNEL.
-
-    aiplatform.googleapis.com exige OAuth2/service account ; les clés API
-    sont systématiquement rejetées avec 401 UNAUTHENTICATED.
-    Ce provider reste présent mais is_configured() retourne toujours False.
-    """
-
-    @property
-    def provider_type(self) -> ProviderType:
-        return ProviderType.VERTEX_API_KEY
-
-    def is_configured(self) -> bool:
-        if os.environ.get(_ENV_KEY):
-            logger.warning(_UNAVAILABLE_MSG)
-        return False
-
-    def list_models(self) -> list[ModelInfo]:
-        raise RuntimeError(_UNAVAILABLE_MSG)
-
-    def generate_content(self, image_bytes: bytes, prompt: str, model_id: str, supports_vision: bool = True) -> str:
-        raise RuntimeError(_UNAVAILABLE_MSG)

backend/app/services/job_runner.py

@@ -129,7 +129,6 @@ async def _run_job_impl(job_id: str, db: AsyncSession) -> None:
             provider=ProviderType(model_db.provider_type),
             supports_vision=model_db.supports_vision,
             last_fetched_at=model_db.updated_at,
-            available_models=[],
         )
 
         # ── 5. Obtenir l'image pour l'IA ─────────────────────────────────────

backend/tests/test_ai_analyzer.py

@@ -71,7 +71,6 @@ def _make_model_config(provider: ProviderType = ProviderType.GOOGLE_AI_STUDIO) -
         provider=provider,
         supports_vision=True,
         last_fetched_at=datetime.now(tz=timezone.utc),
-        available_models=[],
     )
 
 
@@ -189,24 +188,6 @@ def test_build_client_google_ai_studio_missing_env(monkeypatch):
         build_client(ProviderType.GOOGLE_AI_STUDIO)
 
 
-def test_build_client_vertex_api_key(monkeypatch):
-    monkeypatch.setenv("VERTEX_API_KEY", "fake-vertex-key")
-
-    with patch("app.services.ai.client_factory.genai.Client") as mock_cls:
-        mock_cls.return_value = MagicMock()
-        client = build_client(ProviderType.VERTEX_API_KEY)
-
-    mock_cls.assert_called_once_with(vertexai=True, api_key="fake-vertex-key")
-    assert client is mock_cls.return_value
-
-
-def test_build_client_vertex_api_key_missing_env(monkeypatch):
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
-
-    with pytest.raises(RuntimeError, match="VERTEX_API_KEY"):
-        build_client(ProviderType.VERTEX_API_KEY)
-
-
 def test_build_client_vertex_service_account(monkeypatch):
     sa_json = json.dumps({
         "type": "service_account",

backend/tests/test_ai_providers.py

@@ -16,7 +16,6 @@ from app.schemas.model_config import ModelConfig, ModelInfo, ProviderType
 from app.services.ai.base import is_vision_model
 from app.services.ai.model_registry import build_model_config, list_all_models
 from app.services.ai.provider_google_ai import GoogleAIProvider
-from app.services.ai.provider_vertex_key import VertexAPIKeyProvider
 from app.services.ai.provider_vertex_sa import VertexServiceAccountProvider
 
 # ---------------------------------------------------------------------------
@@ -114,7 +113,6 @@ def test_model_config_valid():
         provider=ProviderType.GOOGLE_AI_STUDIO,
         supports_vision=True,
         last_fetched_at=datetime(2026, 3, 17, tzinfo=timezone.utc),
-        available_models=[],
     )
     assert cfg.corpus_id == "corpus-001"
     assert cfg.supports_vision is True
@@ -223,38 +221,6 @@ def test_google_ai_provider_empty_list(monkeypatch):
     assert models == []
 
 
-# ---------------------------------------------------------------------------
-# Tests — VertexAPIKeyProvider
-# ---------------------------------------------------------------------------
-
-def test_vertex_key_provider_not_configured(monkeypatch):
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
-    assert VertexAPIKeyProvider().is_configured() is False
-
-
-def test_vertex_key_provider_always_unavailable_even_with_key(monkeypatch):
-    """aiplatform.googleapis.com rejette les clés API → is_configured() toujours False."""
-    monkeypatch.setenv("VERTEX_API_KEY", "fake-vertex-key")
-    assert VertexAPIKeyProvider().is_configured() is False
-
-
-def test_vertex_key_provider_type():
-    assert VertexAPIKeyProvider().provider_type == ProviderType.VERTEX_API_KEY
-
-
-def test_vertex_key_provider_list_models_raises(monkeypatch):
-    """list_models() et generate_content() lèvent RuntimeError (provider indisponible)."""
-    monkeypatch.setenv("VERTEX_API_KEY", "fake-vertex-key")
-    with pytest.raises(RuntimeError, match="aiplatform"):
-        VertexAPIKeyProvider().list_models()
-
-
-def test_vertex_key_provider_generate_content_raises(monkeypatch):
-    monkeypatch.setenv("VERTEX_API_KEY", "fake-vertex-key")
-    with pytest.raises(RuntimeError, match="aiplatform"):
-        VertexAPIKeyProvider().generate_content(b"img", "prompt", "gemini-2.0-flash")
-
-
 # ---------------------------------------------------------------------------
 # Tests — VertexServiceAccountProvider
 # ---------------------------------------------------------------------------
@@ -348,7 +314,7 @@ def test_vertex_sa_provider_filters_non_generate_content(monkeypatch):
 
 def test_list_all_models_no_providers_configured(monkeypatch):
     monkeypatch.delenv("GOOGLE_AI_STUDIO_API_KEY", raising=False)
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.delenv("VERTEX_SERVICE_ACCOUNT_JSON", raising=False)
     result = list_all_models()
     assert result == []
@@ -356,7 +322,7 @@ def test_list_all_models_no_providers_configured(monkeypatch):
 
 def test_list_all_models_one_provider(monkeypatch):
     monkeypatch.setenv("GOOGLE_AI_STUDIO_API_KEY", "fake-key")
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.delenv("VERTEX_SERVICE_ACCOUNT_JSON", raising=False)
     mock_model = _make_mock_model()
 
@@ -370,11 +336,10 @@ def test_list_all_models_one_provider(monkeypatch):
 
 def test_list_all_models_aggregates_multiple_providers(monkeypatch):
     """Deux providers configurés → les deux listes sont agrégées.
-    VertexAPIKeyProvider est toujours indisponible (aiplatform n'accepte pas les clés).
     On utilise Google AI Studio + Vertex Service Account pour tester l'agrégation.
     """
     monkeypatch.setenv("GOOGLE_AI_STUDIO_API_KEY", "fake-key-ai")
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.setenv("VERTEX_SERVICE_ACCOUNT_JSON", "{}")  # déclenche is_configured()
 
     models_ai = [ModelInfo(
@@ -404,7 +369,7 @@ def test_list_all_models_failing_provider_is_skipped(monkeypatch):
     """Un provider configuré qui échoue est ignoré ; l'autre est retourné."""
     monkeypatch.setenv("GOOGLE_AI_STUDIO_API_KEY", "bad-key")
     monkeypatch.setenv("VERTEX_SERVICE_ACCOUNT_JSON", "{}")
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
 
     models_sa = [ModelInfo(
         model_id="models/gemini-2.0-flash",
@@ -427,7 +392,7 @@ def test_list_all_models_failing_provider_is_skipped(monkeypatch):
 
 def test_build_model_config_valid(monkeypatch):
     monkeypatch.setenv("GOOGLE_AI_STUDIO_API_KEY", "fake-key")
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.delenv("VERTEX_SERVICE_ACCOUNT_JSON", raising=False)
     mock_model = _make_mock_model()
 
@@ -440,13 +405,11 @@ def test_build_model_config_valid(monkeypatch):
     assert cfg.selected_model_display_name == "Gemini 1.5 Pro"
     assert cfg.provider == ProviderType.GOOGLE_AI_STUDIO
     assert cfg.supports_vision is True
-    assert len(cfg.available_models) == 1
-    assert isinstance(cfg.available_models[0], dict)
 
 
 def test_build_model_config_unknown_model(monkeypatch):
     monkeypatch.setenv("GOOGLE_AI_STUDIO_API_KEY", "fake-key")
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.delenv("VERTEX_SERVICE_ACCOUNT_JSON", raising=False)
     mock_model = _make_mock_model()
 
@@ -458,7 +421,7 @@ def test_build_model_config_unknown_model(monkeypatch):
 
 def test_build_model_config_no_providers(monkeypatch):
     monkeypatch.delenv("GOOGLE_AI_STUDIO_API_KEY", raising=False)
-    monkeypatch.delenv("VERTEX_API_KEY", raising=False)
+
     monkeypatch.delenv("VERTEX_SERVICE_ACCOUNT_JSON", raising=False)
     with pytest.raises(ValueError, match="non disponible"):
         build_model_config("corpus-001", "models/gemini-1.5-pro")

backend/tests/test_api_providers.py

@@ -26,21 +26,18 @@ _NOW = datetime.now(timezone.utc)
 
 _PROVIDERS_ALL_UNAVAILABLE = [
     {"provider_type": "google_ai_studio", "display_name": "Google AI Studio", "available": False, "model_count": 0},
-    {"provider_type": "vertex_api_key", "display_name": "Vertex AI (clé API)", "available": False, "model_count": 0},
     {"provider_type": "vertex_service_account", "display_name": "Vertex AI (compte de service)", "available": False, "model_count": 0},
     {"provider_type": "mistral", "display_name": "Mistral AI", "available": False, "model_count": 0},
 ]
 
 _PROVIDERS_GOOGLE_ONLY = [
     {"provider_type": "google_ai_studio", "display_name": "Google AI Studio", "available": True, "model_count": 2},
-    {"provider_type": "vertex_api_key", "display_name": "Vertex AI (clé API)", "available": False, "model_count": 0},
     {"provider_type": "vertex_service_account", "display_name": "Vertex AI (compte de service)", "available": False, "model_count": 0},
     {"provider_type": "mistral", "display_name": "Mistral AI", "available": False, "model_count": 0},
 ]
 
 _PROVIDERS_GOOGLE_AND_MISTRAL = [
     {"provider_type": "google_ai_studio", "display_name": "Google AI Studio", "available": True, "model_count": 3},
-    {"provider_type": "vertex_api_key", "display_name": "Vertex AI (clé API)", "available": False, "model_count": 0},
     {"provider_type": "vertex_service_account", "display_name": "Vertex AI (compte de service)", "available": False, "model_count": 0},
     {"provider_type": "mistral", "display_name": "Mistral AI", "available": True, "model_count": 2},
 ]
@@ -100,7 +97,7 @@ async def test_list_providers_returns_list(async_client, monkeypatch):
 async def test_list_providers_count(async_client, monkeypatch):
     monkeypatch.setattr("app.services.ai.model_registry.get_available_providers", lambda: _PROVIDERS_ALL_UNAVAILABLE)
     data = (await async_client.get("/api/v1/providers")).json()
-    assert len(data) == 4  # 4 providers connus
+    assert len(data) == 3  # 3 providers connus
 
 
 @pytest.mark.asyncio

backend/tests/test_provider_mistral.py

@@ -20,7 +20,6 @@ from app.schemas.model_config import ProviderType
 from app.services.ai.provider_mistral import (
     MistralProvider,
     _MISTRAL_FALLBACK_MODELS,
-    _MISTRAL_VISION_MODELS,  # alias backward-compat
     _model_supports_vision,
 )
 
@@ -256,11 +255,6 @@ def test_list_models_raises_if_not_configured(monkeypatch):
         MistralProvider().list_models()
 
 
-def test_list_models_fallback_backward_compat():
-    """_MISTRAL_VISION_MODELS est un alias de _MISTRAL_FALLBACK_MODELS."""
-    assert _MISTRAL_VISION_MODELS is _MISTRAL_FALLBACK_MODELS
-
-
 # ---------------------------------------------------------------------------
 # generate_content() — bifurcation vision / texte
 # ---------------------------------------------------------------------------