"""RAG retrieval: similarity search over user messages and history lookups."""

from __future__ import annotations

import logging

import numpy as np

from vibe_bot import llm_client
from vibe_bot.config import (
    EMBEDDING_ENDPOINT,
    EMBEDDING_ENDPOINT_KEY,
    EMBEDDING_MODEL,
    SIMILARITY_THRESHOLD,
    TOP_K_RESULTS,
)
from vibe_bot.db.connection import connect

logger = logging.getLogger(__name__)


def search_similar_messages(
    db_path: str,
    query: str,
    top_k: int = TOP_K_RESULTS,
    min_similarity: float = SIMILARITY_THRESHOLD,
) -> list[tuple[str, str, float]]:
    """Search for messages similar to the query using embeddings.

    A single JOIN pulls every user row, its stored embedding, the stored L2
    norm, and its ``_response`` companion. Similarities are one matrix
    multiply over the stored norms — no per-vector renormalization. Rows
    with a missing or zero norm score 0 instead of dividing by zero.
    """
    query_embedding = llm_client.embedding(
        text=query,
        model=EMBEDDING_MODEL,
        url=EMBEDDING_ENDPOINT,
        api_key=EMBEDDING_ENDPOINT_KEY,
    )
    if not query_embedding:
        return []

    query_vector = np.array(query_embedding, dtype=np.float32)
    query_norm = float(np.linalg.norm(query_vector))
    if query_norm == 0:
        return []

    conn = connect(db_path)
    try:
        cursor = conn.cursor()
        cursor.execute(
            """
            SELECT cm.content, r.content, me.embedding, me.norm
            FROM chat_messages cm
            JOIN message_embeddings me ON me.message_id = cm.message_id
            LEFT JOIN chat_messages r ON r.message_id = cm.message_id || '_response'
            WHERE cm.role = 'user'
            """,
        )
        rows = cursor.fetchall()
    finally:
        conn.close()

    if not rows:
        return []

    n_rows = len(rows)
    blobs = [embedding_blob for _c, _r, embedding_blob, _n in rows]
    dim = len(blobs[0]) // 4
    if sum(len(blob) for blob in blobs) == n_rows * dim * 4:
        vectors = np.frombuffer(b"".join(blobs), dtype=np.float32).reshape(n_rows, dim)
    else:
        # Mixed blob lengths (e.g. EMBEDDING_MODEL changed mid-life) can't be
        # batched into one reshape; reconstruct per row, zero-padded (or
        # truncated) to the query dim so the single matrix multiply still works.
        vectors = np.zeros((n_rows, query_vector.size), dtype=np.float32)
        for i, blob in enumerate(blobs):
            row = np.frombuffer(blob, dtype=np.float32)
            k = min(row.size, query_vector.size)
            vectors[i, :k] = row[:k]
    norms = np.array(
        [
            stored_norm if stored_norm is not None else 0.0
            for _c, _r, _b, stored_norm in rows
        ],
        dtype=np.float64,
    )
    safe_norms = np.where(norms > 0, norms, 1.0)
    similarities = vectors @ query_vector / (safe_norms * query_norm)
    similarities = np.where(norms > 0, similarities, 0.0)

    results: list[tuple[str, str, float]] = []
    for (content, response, _blob, _norm), similarity in zip(
        rows, similarities, strict=True
    ):
        if response is None or similarity < min_similarity:
            continue
        results.append((str(content), str(response), float(similarity)))

    results.sort(key=lambda item: item[2], reverse=True)
    return results[:top_k]


def get_bot_history(
    db_path: str, bot_name: str, limit: int = 20
) -> list[tuple[str, str]]:
    """Get message history for a specific custom bot.

    Args:
        bot_name: The name of the custom bot.
        limit: Maximum number of messages to retrieve.

    Returns:
        List of (user_message, bot_response) tuples.

    """
    conn = connect(db_path)
    cursor = conn.cursor()

    logger.debug(
        "Fetching last %d messages for bot %r",
        limit,
        bot_name,
    )
    cursor.execute(
        """
        SELECT message_id, content
        FROM chat_messages
        WHERE bot_name = ? AND message_id NOT LIKE '%%_response'
        ORDER BY timestamp DESC
        LIMIT ?
    """,
        (bot_name, limit),
    )

    conversations: list[tuple[str, str]] = []
    try:
        for message_id, msg_content in cursor.fetchall():
            logger.debug("Finding response for message_id=%s", message_id)
            cursor.execute(
                """
                SELECT content
                FROM chat_messages
                WHERE message_id = ?
                ORDER BY timestamp DESC
                """,
                (f"{message_id}_response",),
            )
            response_row = cursor.fetchone()
            if response_row:
                logger.debug("Found response for message_id=%s", message_id)
                conversations.append((str(msg_content), str(response_row[0])))
            else:
                logger.debug("No response found")
    finally:
        conn.close()

    return conversations


def get_user_history(
    db_path: str, user_id: str, limit: int = 20
) -> list[tuple[str, str]]:
    """Get message history for a specific user."""
    conn = connect(db_path)
    cursor = conn.cursor()

    logger.debug("Fetching last %d user messages", limit)
    cursor.execute(
        """
        SELECT message_id, content
        FROM chat_messages
        WHERE user_id = ? AND role = 'user'
        ORDER BY timestamp DESC
        LIMIT ?
    """,
        (user_id, limit),
    )

    # Format is [user message, bot response]
    conversations: list[tuple[str, str]] = []
    try:
        for message_id, msg_content in cursor.fetchall():
            logger.debug("Finding response for message_id=%s", message_id)
            cursor.execute(
                """
                SELECT content
                FROM chat_messages
                WHERE message_id = ?
                ORDER BY timestamp DESC
                """,
                (f"{message_id}_response",),
            )
            response_row = cursor.fetchone()
            if response_row:
                logger.debug("Found response for message_id=%s", message_id)
                conversations.append((str(msg_content), str(response_row[0])))
            else:
                logger.debug("No response found")
    finally:
        conn.close()

    return conversations