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

backend/model_capabilities.py

@@ -0,0 +1,144 @@
+"""Curated model-capability metadata for the AI assistant.
+
+ObsiGate does not query every provider for the modalities a model supports
+(not all of them expose that information, and the network call is not always
+reliable). Instead a static, curated table maps known model-name patterns to
+capability flags, with per-provider defaults. The UI uses this to show, when a
+model is selected, which features it supports (Chat, Embeddings, Rerank,
+Images, Video, Audio Speech, Audio Transcriptions, Vision).
+
+The table is intentionally conservative: an unknown model falls back to the
+provider default (usually ``chat`` only), so we never claim a capability the
+model may not have.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+# Ordered list of capability keys exposed to the UI. Keep in sync with the
+# frontend ``AI_CAPABILITY_KEYS`` and the i18n ``ai.cap_*`` labels.
+CAPABILITY_KEYS: tuple[str, ...] = (
+    "chat",
+    "embeddings",
+    "rerank",
+    "images",
+    "video",
+    "audio_speech",
+    "audio_transcription",
+    "vision",
+)
+
+
+def _caps(**kwargs: bool) -> dict[str, bool]:
+    """Build a full capability dict (missing flags default to False)."""
+    base = {key: False for key in CAPABILITY_KEYS}
+    base.update(kwargs)
+    return base
+
+
+# Provider defaults, used when no model-name pattern matches.
+_PROVIDER_DEFAULTS: dict[str, dict[str, bool]] = {
+    "deepseek": _caps(chat=True),
+    "openrouter": _caps(chat=True),
+    "gemini": _caps(chat=True, vision=True, embeddings=True, audio_speech=True),
+    "ollama": _caps(chat=True, embeddings=True),
+    "nvidia": _caps(chat=True),
+    "qwencloud": _caps(chat=True),
+    "xiaomi": _caps(chat=True),
+    "mistral": _caps(chat=True, embeddings=True),
+}
+
+# Ordered (substrings, capabilities) rules — the first matching rule wins.
+# More specific modalities are listed before the broad vision/chat rule.
+_MODEL_RULES: list[tuple[tuple[str, ...], dict[str, bool]]] = [
+    # Rerankers.
+    (("rerank", "cross-encoder"), _caps(rerank=True)),
+    # Embedding models (usually not chat-capable).
+    (
+        ("text-embedding", "embed", "bge-", "e5-", "nomic-embed", "gte-"),
+        _caps(embeddings=True),
+    ),
+    # Speech-to-text / transcription.
+    (
+        ("whisper", "transcrib", "-asr", "asr-", "speech-to-text"),
+        _caps(audio_transcription=True),
+    ),
+    # Text-to-speech.
+    (
+        ("-tts", "text-to-speech", "voiceclone", "voicedesign"),
+        _caps(audio_speech=True),
+    ),
+    # Image generation.
+    (
+        ("dall-e", "stable-diffusion", "flux", "imagen", "image-gen"),
+        _caps(images=True),
+    ),
+    # Video generation.
+    (("veo-", "sora", "video-gen", "-video"), _caps(video=True)),
+    # Vision-capable chat models (multimodal input).
+    (
+        (
+            "gpt-4o",
+            "gpt-4.1",
+            "gpt-5",
+            "o3",
+            "o4",
+            "claude-3",
+            "claude-4",
+            "gemini",
+            "qwen-vl",
+            "-vl-",
+            "vl-",
+            "vision",
+            "llava",
+            "pixtral",
+            "internvl",
+            "minicpm-v",
+            "llama-3.2-vision",
+            "mimo-vl",
+        ),
+        _caps(chat=True, vision=True),
+    ),
+]
+
+
+def get_model_capabilities(provider: str, model: str) -> dict[str, bool]:
+    """Return the capability flags for a ``provider``/``model`` pair.
+
+    Args:
+        provider: Provider identifier (e.g. ``"deepseek"``). Case-insensitive.
+        model: Model identifier (e.g. ``"deepseek-chat"``). May be empty, in
+            which case the provider default is returned.
+
+    Returns:
+        A dict with every key of :data:`CAPABILITY_KEYS` and boolean values.
+    """
+    provider = (provider or "").strip().lower()
+    name = (model or "").strip().lower()
+    if name:
+        for needles, caps in _MODEL_RULES:
+            if any(needle in name for needle in needles):
+                return dict(caps)
+    return dict(_PROVIDER_DEFAULTS.get(provider, _caps(chat=True)))
+
+
+def get_capabilities_for_models(
+    provider: str, models: list[str]
+) -> dict[str, dict[str, bool]]:
+    """Return a ``{model: capabilities}`` map for a list of models."""
+    return {model: get_model_capabilities(provider, model) for model in models}
+
+
+def model_supports_vision(provider: str, model: str) -> bool:
+    """True when the given model can accept image input."""
+    return bool(get_model_capabilities(provider, model).get("vision"))
+
+
+def capabilities_payload(provider: str, model: str) -> dict[str, Any]:
+    """Serialize capabilities for API responses."""
+    return {
+        "provider": provider,
+        "model": model,
+        "capabilities": get_model_capabilities(provider, model),
+    }