# rag/main.py
import argparse, sqlite3, sys
from pathlib import Path

from sentence_transformers import SentenceTransformer
from rag.ingest import start_ingest
from rag.search import search_hybrid, search as vec_search

DB_PATH = "./rag.db"
EMBED_MODEL_ID = "Qwen/Qwen3-Embedding-8B"

def open_db():
    db = sqlite3.connect(DB_PATH)
    # speed-ish pragmas
    db.execute("PRAGMA journal_mode=WAL;")
    db.execute("PRAGMA synchronous=NORMAL;")
    return db

def load_st_model():
    # ST handles batching + GPU internally
    return SentenceTransformer(
        EMBED_MODEL_ID,
        model_kwargs={
            "attn_implementation": "flash_attention_2",
            "device_map": "auto",
            "torch_dtype": "float16",
        },
        tokenizer_kwargs={"padding_side": "left"},
    )

def ensure_collection_exists(db, collection: str):
    row = db.execute(
        "SELECT name FROM sqlite_master WHERE type='table' AND name=?",
        (f"vec_{collection}",)
    ).fetchone()
    return bool(row)

def cmd_ingest(args):
    db = open_db()
    st_model = load_st_model()
    path = Path(args.file)

    if not path.exists():
        print(f"File not found: {path}", file=sys.stderr)
        sys.exit(1)

    if args.rebuild and ensure_collection_exists(db, args.collection):
        db.executescript(f"""
            DROP TABLE IF EXISTS chunks_{args.collection};
            DROP TABLE IF EXISTS fts_{args.collection};
            DROP TABLE IF EXISTS vec_{args.collection};
        """)

    stats = start_ingest(
        db, st_model,
        path=path,
        collection=args.collection,
    )
    print(f"Ingested collection={args.collection} :: {stats}")
    db.close()

def cmd_query(args):
    db = open_db()
    st_model = load_st_model()

    coll_ok = ensure_collection_exists(db, args.collection)
    if not coll_ok:
        print(f"Collection '{args.collection}' not found. Ingest first.", file=sys.stderr)
        sys.exit(2)

    if args.simple:
        results = vec_search(db, st_model, args.query, collection=args.collection, k=args.k_final)
    else:
        results = search_hybrid(
            db, st_model, args.query,
            collection=args.collection,
            k_vec=args.k_vec,
            k_bm25=args.k_bm25,
            k_ce=args.k_ce,
            k_final=args.k_final,
            use_mmr=args.mmr,
            mmr_lambda=args.mmr_lambda,
        )

    for rid, txt, score in results:
        print(f"[{rid:05d}] score={score:.3f}\n{txt[:400]}...\n")

    db.close()

def main():
    ap = argparse.ArgumentParser(prog="rag")
    sub = ap.add_subparsers(dest="cmd", required=True)

    # ingest
    ap_ing = sub.add_parser("ingest", help="Parse, chunk, embed, and index a file into a collection")
    ap_ing.add_argument("--file", required=True, help="Path to PDF/TXT to ingest")
    ap_ing.add_argument("--collection", required=True, help="Collection name (e.g. wm_qwen3)")
    ap_ing.add_argument("--rebuild", action="store_true", help="Drop and recreate collection tables")
    ap_ing.set_defaults(func=cmd_ingest)

    # query
    ap_q = sub.add_parser("query", help="Query a collection")
    ap_q.add_argument("--collection", required=True, help="Collection name to search")
    ap_q.add_argument("--query", required=True, help="User query text")
    ap_q.add_argument("--simple", action="store_true", help="Vector-only search (skip reranker)")
    ap_q.add_argument("--mmr", action="store_true", help="Apply MMR after CE")
    ap_q.add_argument("--mmr-lambda", type=float, default=0.7)
    ap_q.add_argument("--k-vec", type=int, default=50)
    ap_q.add_argument("--k-bm25", type=int, default=50)
    ap_q.add_argument("--k-ce", type=int, default=30)
    ap_q.add_argument("--k-final", type=int, default=10)
    ap_q.set_defaults(func=cmd_query)

    args = ap.parse_args()
    args.func(args)

if __name__ == "__main__":
    main()