"""Context retrieval — structured keyword lookup (Free) + chunk search — MIT licensed.

Two retrieval modes for context_chunks:
  Vector search  — cosine similarity over stored embeddings (when available)
  Keyword search — LIKE-based fallback when no embedder is configured

Both modes are called from retrieve_context(); the best available mode is used
automatically so callers need not check EMBEDDING_AVAILABLE themselves.
"""
from __future__ import annotations

import logging
import sqlite3
from dataclasses import dataclass, field
from pathlib import Path

import numpy as np

from app.services.embeddings import (
    EMBEDDING_AVAILABLE,
    cosine_similarity,
    get_embedder,
    unpack_vector,
)

logger = logging.getLogger(__name__)


@dataclass
class RetrievedContext:
    facts: list[dict[str, str]] = field(default_factory=list)
    chunks: list[dict[str, str]] = field(default_factory=list)


# ── Structured fact retrieval (always runs) ───────────────────────────────────

def get_relevant_facts(db_path: Path, query: str) -> list[dict[str, str]]:
    """Keyword match against context_facts. Always runs — Free tier."""
    try:
        conn = sqlite3.connect(str(db_path), timeout=30.0)
        conn.execute("PRAGMA journal_mode=WAL")
        conn.row_factory = sqlite3.Row
        keywords = [w.lower() for w in query.split() if len(w) > 2]
        if not keywords:
            rows = conn.execute(
                "SELECT category, key, value, source FROM context_facts"
                " ORDER BY category LIMIT 20"
            ).fetchall()
        else:
            conditions = " OR ".join(
                "(LOWER(key) LIKE ? OR LOWER(value) LIKE ?)" for _ in keywords
            )
            params: list[str] = []
            for kw in keywords:
                params.extend([f"%{kw}%", f"%{kw}%"])
            rows = conn.execute(
                f"SELECT category, key, value, source FROM context_facts"
                f" WHERE {conditions} ORDER BY category LIMIT 10",
                params,
            ).fetchall()
        conn.close()
        return [dict(r) for r in rows]
    except sqlite3.OperationalError:
        return []


# ── Chunk retrieval: vector path ──────────────────────────────────────────────

def _search_chunks_vector(
    db_path: Path,
    query: str,
    top_k: int = 3,
) -> list[dict[str, str]]:
    """Cosine similarity search over embedded context_chunks.

    Loads all stored embeddings into memory and scores in-process with numpy.
    Skips any chunk whose BLOB dimension does not match the current model dim
    (stale embeddings from a previous model — they will be re-embedded on the
    next document upload).

    Returns at most *top_k* results ordered by similarity descending.
    """
    embedder = get_embedder()
    if embedder is None:
        return []

    try:
        query_vec: np.ndarray = embedder.embed(query)
        model_dim: int = embedder.dim
    except Exception as exc:
        logger.warning("Query embedding failed: %s", exc)
        return []

    try:
        conn = sqlite3.connect(str(db_path), timeout=30.0)
        conn.execute("PRAGMA journal_mode=WAL")
        conn.row_factory = sqlite3.Row
        rows = conn.execute(
            "SELECT cc.id, cc.text, cc.embedding, cd.filename"
            " FROM context_chunks cc"
            " JOIN context_documents cd ON cc.document_id = cd.id"
            " WHERE cc.embedding IS NOT NULL"
        ).fetchall()
        conn.close()
    except sqlite3.OperationalError:
        return []

    scored: list[tuple[float, dict[str, str]]] = []
    for row in rows:
        blob: bytes = row["embedding"]
        # Guard against blobs from a different-dimension model
        if len(blob) // 4 != model_dim:
            continue
        try:
            chunk_vec = unpack_vector(blob)
            score = cosine_similarity(query_vec, chunk_vec)
            scored.append((score, {"text": row["text"], "filename": row["filename"]}))
        except Exception:
            continue

    scored.sort(key=lambda t: t[0], reverse=True)
    return [item for _, item in scored[:top_k]]


# ── Chunk retrieval: keyword fallback ─────────────────────────────────────────

def _search_chunks_keyword(db_path: Path, query: str) -> list[dict[str, str]]:
    """LIKE-based keyword search across context_chunks. Fallback when no embedder."""
    try:
        conn = sqlite3.connect(str(db_path), timeout=30.0)
        conn.execute("PRAGMA journal_mode=WAL")
        conn.row_factory = sqlite3.Row
        keywords = [w.lower() for w in query.split() if len(w) > 2][:5]
        if not keywords:
            conn.close()
            return []
        conditions = " OR ".join("LOWER(cc.text) LIKE ?" for _ in keywords)
        params = [f"%{kw}%" for kw in keywords]
        rows = conn.execute(
            f"SELECT cc.text, cd.filename FROM context_chunks cc"
            f" JOIN context_documents cd ON cc.document_id = cd.id"
            f" WHERE {conditions} LIMIT 3",
            params,
        ).fetchall()
        conn.close()
        return [{"text": r["text"], "filename": r["filename"]} for r in rows]
    except sqlite3.OperationalError:
        return []


# ── Public interface ──────────────────────────────────────────────────────────

def retrieve_context(db_path: Path, query: str) -> RetrievedContext:
    """Retrieve structured facts and relevant chunks for a query.

    Chunk retrieval uses vector search when an embedder is available and at
    least one embedded chunk exists; falls back to keyword search otherwise.
    """
    facts = get_relevant_facts(db_path, query)

    if EMBEDDING_AVAILABLE:
        chunks = _search_chunks_vector(db_path, query)
        if not chunks:
            # Vector search returned nothing (no embedded chunks yet) — fall back.
            chunks = _search_chunks_keyword(db_path, query)
    else:
        chunks = _search_chunks_keyword(db_path, query)

    return RetrievedContext(facts=facts, chunks=chunks)


def format_context_block(ctx: RetrievedContext) -> str | None:
    """Format context for injection into an LLM prompt. Returns None when empty."""
    lines: list[str] = []
    if ctx.facts:
        lines.append("Known environment facts:")
        for f in ctx.facts:
            lines.append(f"  [{f['category']}] {f['key']}: {f['value']}")
    if ctx.chunks:
        lines.append("Relevant documentation:")
        for c in ctx.chunks:
            lines.append(f"  [{c['filename']}] {c['text'][:200]}")
    return "\n".join(lines) if lines else None