"""Persistent memory: SQLite storage + brute-force cosine recall over embeddings.

Each exchange is stored with its OpenAI embedding as a float32 BLOB. Recall
loads all embeddings (optionally scoped to a session) into a matrix and
returns the top-k by cosine similarity. Brute force is fine up to tens of
thousands of rows; swap in a vector index when that stops being true.
"""
from __future__ import annotations

import json
import sqlite3
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path

import numpy as np

from lyra import llm
from lyra.config import load

SCHEMA = """
CREATE TABLE IF NOT EXISTS exchanges (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    session_id TEXT NOT NULL,
    role TEXT NOT NULL,
    content TEXT NOT NULL,
    embedding BLOB NOT NULL,
    created_at TEXT NOT NULL
);
CREATE INDEX IF NOT EXISTS idx_session_created ON exchanges(session_id, created_at);

CREATE TABLE IF NOT EXISTS sessions (
    id TEXT PRIMARY KEY,
    name TEXT,
    created_at TEXT NOT NULL
);

-- One compacted "gist" per session. last_exchange_id marks how far the summary
-- covers, so we know when enough new turns have accumulated to re-summarize.
CREATE TABLE IF NOT EXISTS summaries (
    session_id TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    embedding BLOB NOT NULL,
    last_exchange_id INTEGER NOT NULL,
    created_at TEXT NOT NULL
);

-- Derived semantic memory: standing facts about the user, distilled from the
-- session gists by the consolidation pass. Single row (id='self').
CREATE TABLE IF NOT EXISTS profile (
    id TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    sessions_covered INTEGER NOT NULL,
    updated_at TEXT NOT NULL
);

-- Temporal memory: one "what was happening" digest per calendar month, rolled
-- up from that month's session gists. month is "YYYY-MM".
CREATE TABLE IF NOT EXISTS eras (
    month TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    embedding BLOB NOT NULL,
    session_count INTEGER NOT NULL,
    created_at TEXT NOT NULL
);

-- The current narrative: time-aware arc/trends/callbacks (vs the timeless
-- profile). Distilled from profile + recent eras. Single row (id='current').
CREATE TABLE IF NOT EXISTS narrative (
    id TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    updated_at TEXT NOT NULL
);

-- Autonomy Core: Lyra's evolving self-state (mood, energy, her own first-person
-- self-narrative, reflections). Stored as a JSON blob. Single row (id='lyra').
CREATE TABLE IF NOT EXISTS self_state (
    id TEXT PRIMARY KEY,
    data TEXT NOT NULL,
    updated_at TEXT NOT NULL
);
"""

_conn: sqlite3.Connection | None = None
_conn_path: Path | None = None


def _connection() -> sqlite3.Connection:
    """Lazily open the SQLite connection. Reopens if LYRA_DB_PATH changed (for tests)."""
    global _conn, _conn_path
    cfg = load()
    if _conn is None or _conn_path != cfg.db_path:
        if _conn is not None:
            _conn.close()
        cfg.db_path.parent.mkdir(parents=True, exist_ok=True)
        # check_same_thread=False: the web server runs blocking work in a thread
        # pool, so the singleton connection is touched from threads other than
        # the one that created it. Safe here under single-user, low-concurrency use.
        _conn = sqlite3.connect(cfg.db_path, check_same_thread=False)
        _conn.row_factory = sqlite3.Row
        # WAL + a busy timeout so a separate dream-cycle process can read/write
        # alongside the web server without tripping "database is locked".
        _conn.execute("PRAGMA busy_timeout=5000")
        _conn.execute("PRAGMA journal_mode=WAL")
        _conn.executescript(SCHEMA)
        _conn_path = cfg.db_path
    return _conn


@dataclass
class Exchange:
    id: int
    session_id: str
    role: str
    content: str
    created_at: str
    score: float | None = None


@dataclass
class Summary:
    session_id: str
    content: str
    last_exchange_id: int
    created_at: str
    score: float | None = None


@dataclass
class Era:
    month: str  # "YYYY-MM"
    content: str
    session_count: int
    created_at: str
    score: float | None = None


def _to_blob(vec: list[float]) -> bytes:
    return np.asarray(vec, dtype=np.float32).tobytes()


def _from_blob(blob: bytes) -> np.ndarray:
    return np.frombuffer(blob, dtype=np.float32)


def remember(session_id: str, role: str, content: str) -> int:
    """Embed and persist a single exchange. Returns the new row id."""
    [embedding] = llm.embed([content])
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        cur = conn.execute(
            "INSERT INTO exchanges (session_id, role, content, embedding, created_at) "
            "VALUES (?, ?, ?, ?, ?)",
            (session_id, role, content, _to_blob(embedding), now),
        )
    return int(cur.lastrowid)


def add_exchanges_bulk(session_id: str, rows: list[tuple[str, str, list[float], str]]) -> int:
    """Insert many pre-embedded exchanges at once.

    Each row is (role, content, embedding, created_at). Used by the importer to
    avoid one INSERT (and one embed round-trip) per message. Returns row count.
    """
    conn = _connection()
    with conn:
        conn.executemany(
            "INSERT INTO exchanges (session_id, role, content, embedding, created_at) "
            "VALUES (?, ?, ?, ?, ?)",
            [(session_id, role, content, _to_blob(emb), ca) for role, content, emb, ca in rows],
        )
    return len(rows)


def recent(session_id: str, n: int = 10) -> list[Exchange]:
    """Last `n` exchanges from a session, oldest first."""
    conn = _connection()
    rows = conn.execute(
        "SELECT id, session_id, role, content, created_at FROM exchanges "
        "WHERE session_id = ? ORDER BY id DESC LIMIT ?",
        (session_id, n),
    ).fetchall()
    return [
        Exchange(
            id=r["id"],
            session_id=r["session_id"],
            role=r["role"],
            content=r["content"],
            created_at=r["created_at"],
        )
        for r in reversed(rows)
    ]


def ensure_session(session_id: str, name: str | None = None) -> None:
    """Create the session row if absent; set its name if one is given."""
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO sessions (id, name, created_at) VALUES (?, ?, ?) "
            "ON CONFLICT(id) DO NOTHING",
            (session_id, name, now),
        )
        if name is not None:
            conn.execute("UPDATE sessions SET name = ? WHERE id = ?", (name, session_id))


def list_sessions() -> list[dict]:
    """All known sessions (named rows + any session that has exchanges), newest first."""
    conn = _connection()
    rows = conn.execute(
        """
        SELECT s.id AS id,
               s.name AS name,
               COALESCE(s.created_at, MIN(e.created_at)) AS created_at
        FROM sessions s
        LEFT JOIN exchanges e ON e.session_id = s.id
        GROUP BY s.id
        UNION
        SELECT e.session_id AS id, NULL AS name, MIN(e.created_at) AS created_at
        FROM exchanges e
        WHERE e.session_id NOT IN (SELECT id FROM sessions)
        GROUP BY e.session_id
        ORDER BY created_at DESC
        """
    ).fetchall()
    return [{"id": r["id"], "name": r["name"]} for r in rows]


def history(session_id: str) -> list[Exchange]:
    """Full conversation for a session, oldest first."""
    conn = _connection()
    rows = conn.execute(
        "SELECT id, session_id, role, content, created_at FROM exchanges "
        "WHERE session_id = ? ORDER BY id ASC",
        (session_id,),
    ).fetchall()
    return [
        Exchange(
            id=r["id"],
            session_id=r["session_id"],
            role=r["role"],
            content=r["content"],
            created_at=r["created_at"],
        )
        for r in rows
    ]


def delete_session(session_id: str) -> None:
    """Remove a session and all its exchanges."""
    conn = _connection()
    with conn:
        conn.execute("DELETE FROM exchanges WHERE session_id = ?", (session_id,))
        conn.execute("DELETE FROM sessions WHERE id = ?", (session_id,))
        conn.execute("DELETE FROM summaries WHERE session_id = ?", (session_id,))


def recall(query: str, k: int = 5, session_id: str | None = None) -> list[Exchange]:
    """Top-k exchanges semantically similar to `query`, optionally scoped to a session."""
    [q_vec] = llm.embed([query])
    q = np.asarray(q_vec, dtype=np.float32)

    conn = _connection()
    sql = "SELECT id, session_id, role, content, embedding, created_at FROM exchanges"
    params: tuple = ()
    if session_id is not None:
        sql += " WHERE session_id = ?"
        params = (session_id,)
    rows = conn.execute(sql, params).fetchall()
    if not rows:
        return []

    matrix = np.stack([_from_blob(r["embedding"]) for r in rows])
    norms = np.linalg.norm(matrix, axis=1)
    scores = (matrix @ q) / (norms * np.linalg.norm(q) + 1e-9)

    top_idx = np.argsort(scores)[::-1][:k]
    return [
        Exchange(
            id=rows[i]["id"],
            session_id=rows[i]["session_id"],
            role=rows[i]["role"],
            content=rows[i]["content"],
            created_at=rows[i]["created_at"],
            score=float(scores[i]),
        )
        for i in top_idx
    ]


# --- Summary tier (compacted per-session gists) ---


def store_summary(session_id: str, content: str, last_exchange_id: int) -> None:
    """Embed and persist the gist of a session, replacing any prior summary."""
    [embedding] = llm.embed([content])
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO summaries (session_id, content, embedding, last_exchange_id, created_at) "
            "VALUES (?, ?, ?, ?, ?) "
            "ON CONFLICT(session_id) DO UPDATE SET "
            "content=excluded.content, embedding=excluded.embedding, "
            "last_exchange_id=excluded.last_exchange_id, created_at=excluded.created_at",
            (session_id, content, _to_blob(embedding), last_exchange_id, now),
        )


def get_summary(session_id: str) -> Summary | None:
    conn = _connection()
    r = conn.execute(
        "SELECT session_id, content, last_exchange_id, created_at FROM summaries "
        "WHERE session_id = ?",
        (session_id,),
    ).fetchone()
    if r is None:
        return None
    return Summary(
        session_id=r["session_id"],
        content=r["content"],
        last_exchange_id=r["last_exchange_id"],
        created_at=r["created_at"],
    )


def unsummarized_count(session_id: str) -> int:
    """How many exchanges in this session are newer than its current summary."""
    conn = _connection()
    summary = get_summary(session_id)
    cutoff = summary.last_exchange_id if summary else 0
    r = conn.execute(
        "SELECT COUNT(*) AS n FROM exchanges WHERE session_id = ? AND id > ?",
        (session_id, cutoff),
    ).fetchone()
    return int(r["n"])


def list_summaries() -> list[Summary]:
    """Every session gist (for the profile/era consolidation passes)."""
    conn = _connection()
    rows = conn.execute(
        "SELECT session_id, content, last_exchange_id, created_at FROM summaries "
        "ORDER BY created_at ASC"
    ).fetchall()
    return [
        Summary(
            session_id=r["session_id"],
            content=r["content"],
            last_exchange_id=r["last_exchange_id"],
            created_at=r["created_at"],
        )
        for r in rows
    ]


def set_profile(content: str, sessions_covered: int, profile_id: str = "self") -> None:
    """Store/replace the derived semantic profile."""
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO profile (id, content, sessions_covered, updated_at) "
            "VALUES (?, ?, ?, ?) "
            "ON CONFLICT(id) DO UPDATE SET content=excluded.content, "
            "sessions_covered=excluded.sessions_covered, updated_at=excluded.updated_at",
            (profile_id, content, sessions_covered, now),
        )


def get_profile(profile_id: str = "self") -> str | None:
    conn = _connection()
    r = conn.execute("SELECT content FROM profile WHERE id = ?", (profile_id,)).fetchone()
    return r["content"] if r else None


def profile_sessions_covered(profile_id: str = "self") -> int:
    """How many session gists the current profile was built from (0 if none)."""
    conn = _connection()
    r = conn.execute(
        "SELECT sessions_covered FROM profile WHERE id = ?", (profile_id,)
    ).fetchone()
    return int(r["sessions_covered"]) if r else 0


def backlog_stats(ripe_threshold: int = 20) -> dict:
    """Snapshot of the consolidation backlog, for the dream cycle to sense.

    Returns, in one pass over the exchanges: how many sessions have any
    unsummarized turns ("dirty"), how many are "ripe" (never summarized, or
    >= `ripe_threshold` new turns since their last summary), the total
    unsummarized exchanges, and the high-water exchange id (to detect new
    activity since the previous cycle).
    """
    conn = _connection()
    rows = conn.execute(
        """
        SELECT
            SUM(CASE WHEN e.id > COALESCE(su.last_exchange_id, 0) THEN 1 ELSE 0 END)
                AS unsummarized,
            (su.session_id IS NULL) AS no_summary
        FROM exchanges e
        LEFT JOIN summaries su ON su.session_id = e.session_id
        GROUP BY e.session_id
        """
    ).fetchall()
    dirty = ripe = unsummarized_total = 0
    for r in rows:
        u = int(r["unsummarized"] or 0)
        unsummarized_total += u
        if u > 0:
            dirty += 1
            if r["no_summary"] or u >= ripe_threshold:
                ripe += 1
    mx = conn.execute("SELECT COALESCE(MAX(id), 0) AS m FROM exchanges").fetchone()["m"]
    return {
        "sessions": len(rows),
        "dirty": dirty,
        "ripe": ripe,
        "unsummarized_total": unsummarized_total,
        "max_exchange_id": int(mx),
    }


# --- Era tier (per-month temporal rollups) ---


def summaries_by_month() -> dict[str, list[str]]:
    """Map "YYYY-MM" -> list of session gists for sessions that occurred that month.

    A session's month comes from its earliest exchange timestamp (real ChatGPT
    dates for imported sessions), not when it was summarized.
    """
    conn = _connection()
    rows = conn.execute(
        """
        SELECT substr(MIN(e.created_at), 1, 7) AS month, s.content AS content
        FROM summaries s JOIN exchanges e ON e.session_id = s.session_id
        GROUP BY s.session_id
        """
    ).fetchall()
    out: dict[str, list[str]] = {}
    for r in rows:
        out.setdefault(r["month"], []).append(r["content"])
    return out


def store_era(month: str, content: str, session_count: int) -> None:
    """Embed and persist a month's digest, replacing any prior one."""
    [embedding] = llm.embed([content])
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO eras (month, content, embedding, session_count, created_at) "
            "VALUES (?, ?, ?, ?, ?) "
            "ON CONFLICT(month) DO UPDATE SET content=excluded.content, "
            "embedding=excluded.embedding, session_count=excluded.session_count, "
            "created_at=excluded.created_at",
            (month, content, _to_blob(embedding), session_count, now),
        )


def list_eras() -> list[Era]:
    """All month digests, chronological."""
    conn = _connection()
    rows = conn.execute(
        "SELECT month, content, session_count, created_at FROM eras ORDER BY month ASC"
    ).fetchall()
    return [
        Era(month=r["month"], content=r["content"],
            session_count=r["session_count"], created_at=r["created_at"])
        for r in rows
    ]


def set_narrative(content: str, narrative_id: str = "current") -> None:
    """Store/replace the current narrative."""
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO narrative (id, content, updated_at) VALUES (?, ?, ?) "
            "ON CONFLICT(id) DO UPDATE SET content=excluded.content, updated_at=excluded.updated_at",
            (narrative_id, content, now),
        )


def get_narrative(narrative_id: str = "current") -> str | None:
    conn = _connection()
    r = conn.execute("SELECT content FROM narrative WHERE id = ?", (narrative_id,)).fetchone()
    return r["content"] if r else None


def get_self_state(state_id: str = "lyra") -> dict | None:
    conn = _connection()
    r = conn.execute("SELECT data FROM self_state WHERE id = ?", (state_id,)).fetchone()
    return json.loads(r["data"]) if r else None


def set_self_state(state: dict, state_id: str = "lyra") -> None:
    now = datetime.now(timezone.utc).isoformat()
    conn = _connection()
    with conn:
        conn.execute(
            "INSERT INTO self_state (id, data, updated_at) VALUES (?, ?, ?) "
            "ON CONFLICT(id) DO UPDATE SET data=excluded.data, updated_at=excluded.updated_at",
            (state_id, json.dumps(state), now),
        )


def recall_eras(query: str, k: int = 2) -> list[Era]:
    """Top-k month digests most similar to `query` (time-based context)."""
    [q_vec] = llm.embed([query])
    q = np.asarray(q_vec, dtype=np.float32)
    conn = _connection()
    rows = conn.execute(
        "SELECT month, content, embedding, session_count, created_at FROM eras"
    ).fetchall()
    if not rows:
        return []
    matrix = np.stack([_from_blob(r["embedding"]) for r in rows])
    norms = np.linalg.norm(matrix, axis=1)
    scores = (matrix @ q) / (norms * np.linalg.norm(q) + 1e-9)
    top_idx = np.argsort(scores)[::-1][:k]
    return [
        Era(month=rows[i]["month"], content=rows[i]["content"],
            session_count=rows[i]["session_count"], created_at=rows[i]["created_at"],
            score=float(scores[i]))
        for i in top_idx
    ]


def recall_summaries(query: str, k: int = 3, exclude_session: str | None = None) -> list[Summary]:
    """Top-k session summaries most similar to `query` (the long-term gist tier)."""
    [q_vec] = llm.embed([query])
    q = np.asarray(q_vec, dtype=np.float32)

    conn = _connection()
    sql = "SELECT session_id, content, embedding, last_exchange_id, created_at FROM summaries"
    params: tuple = ()
    if exclude_session is not None:
        sql += " WHERE session_id != ?"
        params = (exclude_session,)
    rows = conn.execute(sql, params).fetchall()
    if not rows:
        return []

    matrix = np.stack([_from_blob(r["embedding"]) for r in rows])
    norms = np.linalg.norm(matrix, axis=1)
    scores = (matrix @ q) / (norms * np.linalg.norm(q) + 1e-9)

    top_idx = np.argsort(scores)[::-1][:k]
    return [
        Summary(
            session_id=rows[i]["session_id"],
            content=rows[i]["content"],
            last_exchange_id=rows[i]["last_exchange_id"],
            created_at=rows[i]["created_at"],
            score=float(scores[i]),
        )
        for i in top_idx
    ]