"""
Memory system types — mirrors OpenClaw's src/memory/types.ts.
"""

from dataclasses import dataclass, field
from enum import Enum


class MemorySource(str, Enum):
    """Source of a memory entry — either workspace markdown or session logs."""
    MEMORY = "memory"
    SESSIONS = "sessions"


@dataclass
class MemorySearchResult:
    """
    A single search result from the memory system.
    Mirrors OpenClaw's MemorySearchResult type.
    """
    path: str
    start_line: int
    end_line: int
    score: float
    snippet: str
    source: MemorySource
    citation: str | None = None


@dataclass
class MemoryChunk:
    """
    A chunk of text extracted from a markdown file.
    Mirrors OpenClaw's MemoryChunk from internal.ts.
    """
    start_line: int
    end_line: int
    text: str
    hash: str


@dataclass
class MemoryFileEntry:
    """
    Metadata about an indexed markdown file.
    Mirrors OpenClaw's MemoryFileEntry from internal.ts.
    """
    path: str           # relative path within workspace
    abs_path: str       # absolute filesystem path
    mtime_ms: float     # modification time (ms since epoch)
    size: int           # file size in bytes
    hash: str           # SHA-256 of file content


@dataclass
class SessionFileEntry:
    """
    Metadata about an indexed session transcript file.
    Mirrors OpenClaw's SessionFileEntry from session-files.ts.
    """
    path: str           # relative path (sessions/<filename>)
    abs_path: str       # absolute filesystem path
    mtime_ms: float
    size: int
    hash: str
    content: str        # extracted text content
    line_map: list[int] = field(default_factory=list)


@dataclass
class MemoryConfig:
    """
    Configuration for the memory system.
    """
    # Workspace directory containing SOUL.md, USER.md, MEMORY.md, etc.
    workspace_dir: str = "~/.aetheel/workspace"

    # SQLite database path (created automatically)
    db_path: str = "~/.aetheel/memory.db"

    # Chunking
    chunk_tokens: int = 512
    chunk_overlap: int = 50

    # Search
    max_results: int = 10
    min_score: float = 0.1
    vector_weight: float = 0.7
    text_weight: float = 0.3

    # Embedding
    embedding_model: str = "BAAI/bge-small-en-v1.5"
    embedding_dims: int = 384

    # Sync
    watch: bool = True
    watch_debounce_ms: int = 2000
    sync_on_search: bool = True

    # Session logs
    sessions_dir: str | None = None  # defaults to workspace_dir/daily/

    # Sources to index
    sources: list[str] = field(default_factory=lambda: ["memory", "sessions"])