"""Data models for the Memory System."""

from __future__ import annotations

import uuid
from dataclasses import dataclass, field, asdict
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional


class MemoryTier(str, Enum):
    """Which memory layer an entry belongs to."""
    SESSION = "session"      # short-term / conversation context
    EPISODIC = "episodic"    # mid-term / past tasks & events
    SEMANTIC = "semantic"    # long-term / vector-backed knowledge


@dataclass
class MemoryEntry:
    """A single memory record stored across tiers."""
    id: str = field(default_factory=lambda: uuid.uuid4().hex[:12])
    content: str = ""
    title: str = ""
    tier: MemoryTier = MemoryTier.SESSION
    tags: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    importance: float = 0.5          # 0.0 – 1.0
    access_count: int = 0
    created_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
    updated_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
    session_id: Optional[str] = None  # groups session memories
    source: str = ""                  # origin of the memory

    # ── helpers ──────────────────────────────────────────────
    def to_dict(self) -> Dict[str, Any]:
        d = asdict(self)
        d["tier"] = self.tier.value
        return d

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "MemoryEntry":
        data = dict(data)  # shallow copy
        if "tier" in data and isinstance(data["tier"], str):
            data["tier"] = MemoryTier(data["tier"])
        return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})

    def to_markdown(self) -> str:
        """Render as a Markdown document with YAML front-matter."""
        lines = [
            "---",
            f"id: {self.id}",
            f"title: \"{self.title}\"",
            f"tier: {self.tier.value}",
            f"tags: [{', '.join(self.tags)}]",
            f"importance: {self.importance}",
            f"access_count: {self.access_count}",
            f"created_at: {self.created_at}",
            f"updated_at: {self.updated_at}",
        ]
        if self.session_id:
            lines.append(f"session_id: {self.session_id}")
        if self.source:
            lines.append(f"source: \"{self.source}\"")
        if self.metadata:
            import json
            lines.append(f"metadata: {json.dumps(self.metadata)}")
        lines.append("---")
        lines.append("")
        lines.append(self.content)
        return "\n".join(lines)

    @classmethod
    def from_markdown(cls, text: str) -> "MemoryEntry":
        """Parse a Markdown document with YAML front-matter."""
        import re, json as _json

        fm_match = re.match(r"^---\n(.*?)\n---\n?(.*)", text, re.DOTALL)
        if not fm_match:
            return cls(content=text)

        front, body = fm_match.group(1), fm_match.group(2).strip()
        data: Dict[str, Any] = {"content": body}

        for line in front.splitlines():
            line = line.strip()
            if not line or ":" not in line:
                continue
            key, _, val = line.partition(":")
            key = key.strip()
            val = val.strip().strip('"')

            if key == "tags":
                # parse [tag1, tag2]
                inner = val.strip("[]")
                data["tags"] = [t.strip() for t in inner.split(",") if t.strip()]
            elif key == "importance":
                data["importance"] = float(val)
            elif key == "access_count":
                data["access_count"] = int(val)
            elif key == "metadata":
                try:
                    data["metadata"] = _json.loads(val)
                except _json.JSONDecodeError:
                    data["metadata"] = {}
            else:
                data[key] = val

        return cls.from_dict(data)


@dataclass
class SearchResult:
    """Wrapper returned by semantic search."""
    entry: MemoryEntry
    score: float = 0.0       # similarity / relevance
    distance: float = 0.0    # raw distance from vector DB