feat(ai): commandes @/ & skills, analyse d'images et capacites des modeles (#81)

backend/ai_routes.py

@@ -2,7 +2,7 @@
 
 import logging
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel, Field
 
 from backend.ai import (
@@ -25,6 +25,8 @@ from backend.ai import (
     ai_translate,
     get_default_provider,
 )
+from backend.auth.middleware import require_auth
+from backend.model_capabilities import get_model_capabilities
 from backend.schemas import AIStatusResponse
 
 logger = logging.getLogger("obsigate.ai_routes")
@@ -232,3 +234,32 @@ async def api_inline_complete(req: AIRequest):
 async def api_to_canvas(req: AIRequest):
     """Convert to Mermaid diagram or outline."""
     return await _handle(ai_convert_to_canvas, req)
+
+
+class ModelCapabilitiesResponse(BaseModel):
+    """Capabilities of a single provider/model pair."""
+
+    provider: str
+    model: str
+    capabilities: dict[str, bool] = Field(
+        description="Flags: chat, embeddings, rerank, images, video, "
+                    "audio_speech, audio_transcription, vision",
+    )
+
+
+@router.get("/model-capabilities", response_model=ModelCapabilitiesResponse)
+async def api_model_capabilities(
+    provider: str = Query(..., description="Provider identifier"),
+    model: str = Query("", description="Model identifier (optional)"),
+    current_user=Depends(require_auth),
+):
+    """Return the curated capability flags for a provider/model pair.
+
+    The table is static (see ``backend.model_capabilities``) so the answer is
+    available offline and never depends on a provider API call.
+    """
+    return {
+        "provider": provider,
+        "model": model,
+        "capabilities": get_model_capabilities(provider, model),
+    }