# core/skill_schema.py
"""
Skill Store – Structure de données pour les compétences persistantes.
Chaque skill est un morceau de code validé, versionné, et réutilisable.

Utilisé par :
- VectorMemory (indexation et recherche)
- FableEngine (enregistrement automatique)
- App (commandes @skill)
"""

import uuid
from datetime import datetime
from typing import List, Optional, Dict, Any

from pydantic import BaseModel, Field


# ─── Dépendances ────────────────────────────────────────────────────────────

class SkillDependency(BaseModel):
    """Dépendance d'un skill (bibliothèque Python)."""
    name: str
    version: Optional[str] = None  # ex: ">=1.0.0", "==2.1.0"

    def __str__(self) -> str:
        return f"{self.name}{self.version or ''}"

    def to_dict(self) -> Dict[str, Any]:
        return {"name": self.name, "version": self.version}


# ─── Métadonnées ────────────────────────────────────────────────────────────

class SkillMetadata(BaseModel):
    """Métadonnées descriptives d'un skill."""
    name: str
    description: str
    category: str  # "web", "algorithm", "data", "ml", "utility"
    version: str = "1.0.0"
    author: str = "VORTEX"
    created_at: datetime = Field(default_factory=datetime.now)
    updated_at: datetime = Field(default_factory=datetime.now)
    tags: List[str] = []
    dependencies: List[SkillDependency] = []
    test_status: str = "passed"  # "passed", "failed", "untested"

    def to_dict(self) -> Dict[str, Any]:
        return {
            "name": self.name,
            "description": self.description,
            "category": self.category,
            "version": self.version,
            "author": self.author,
            "created_at": self.created_at.isoformat(),
            "updated_at": self.updated_at.isoformat(),
            "tags": self.tags,
            "dependencies": [d.to_dict() for d in self.dependencies],
            "test_status": self.test_status,
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "SkillMetadata":
        """Reconstruit un SkillMetadata à partir d'un dictionnaire."""
        return cls(
            name=data.get("name", "unknown"),
            description=data.get("description", ""),
            category=data.get("category", "general"),
            version=data.get("version", "1.0.0"),
            author=data.get("author", "VORTEX"),
            created_at=datetime.fromisoformat(data["created_at"]) if data.get("created_at") else datetime.now(),
            updated_at=datetime.fromisoformat(data["updated_at"]) if data.get("updated_at") else datetime.now(),
            tags=data.get("tags", []),
            dependencies=[SkillDependency(**d) for d in data.get("dependencies", [])],
            test_status=data.get("test_status", "untested"),
        )


# ─── Skill complet ─────────────────────────────────────────────────────────

class Skill(BaseModel):
    """Un skill complet : métadonnées + code + tests + score."""
    id: str = Field(default_factory=lambda: str(uuid.uuid4())[:8])
    metadata: SkillMetadata
    code: str
    tests: Optional[str] = None
    score: float = 0.0  # Qualité (0-1)

    def to_text(self) -> str:
        """Construit le texte indexé dans le RAG."""
        lines = [
            f"Skill: {self.metadata.name}",
            f"Version: {self.metadata.version}",
            f"Catégorie: {self.metadata.category}",
            f"Description: {self.metadata.description}",
        ]
        if self.metadata.tags:
            lines.append(f"Tags: {', '.join(self.metadata.tags)}")
        if self.metadata.dependencies:
            lines.append(f"Dépendances: {', '.join(str(d) for d in self.metadata.dependencies)}")
        if self.tests:
            lines.append("--- Tests ---")
            lines.append(self.tests[:300])
        lines.append("--- Code ---")
        lines.append(self.code[:500])
        return "\n".join(lines)

    def to_metadata(self) -> Dict[str, Any]:
        """Construit les métadonnées pour le RAG (ChromaDB)."""
        base = self.metadata.to_dict()
        return {
            "type": "skill",
            "name": base["name"],
            "version": base["version"],
            "category": base["category"],
            "tags": ",".join(base["tags"]),
            "dependencies": ",".join([str(d) for d in self.metadata.dependencies]),
            "score": self.score,
            "test_status": base["test_status"],
            "created_at": base["created_at"],
            "author": base["author"],
        }

    @classmethod
    def from_rag(cls, doc_id: str, text: str, metadata: Dict[str, Any]) -> "Skill":
        """
        Reconstruit un Skill depuis les données d'un document RAG.
        """
        # Métadonnées de base
        meta = SkillMetadata(
            name=metadata.get("name", "unknown"),
            description=metadata.get("description", ""),
            category=metadata.get("category", "general"),
            version=metadata.get("version", "1.0.0"),
            author=metadata.get("author", "VORTEX"),
            tags=metadata.get("tags", "").split(",") if metadata.get("tags") else [],
            dependencies=[
                SkillDependency(name=d.split(">=")[0].strip())
                for d in metadata.get("dependencies", "").split(",") if d
            ],
            test_status=metadata.get("test_status", "unknown"),
        )
        return cls(
            id=doc_id,
            metadata=meta,
            code=text,
            score=float(metadata.get("score", 0.0)),
            tests=None,
        )

    def to_dict(self) -> Dict[str, Any]:
        """Sérialisation complète en dictionnaire."""
        return {
            "id": self.id,
            "metadata": self.metadata.to_dict(),
            "code": self.code,
            "tests": self.tests,
            "score": self.score,
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "Skill":
        """Reconstruit un Skill à partir d'un dictionnaire."""
        return cls(
            id=data.get("id", str(uuid.uuid4())[:8]),
            metadata=SkillMetadata.from_dict(data.get("metadata", {})),
            code=data.get("code", ""),
            tests=data.get("tests"),
            score=data.get("score", 0.0),
        )

    def __repr__(self) -> str:
        return f"<Skill id={self.id} name={self.metadata.name} score={self.score:.2f}>"