"""
Minimal end-to-end retrieval example for the OPERA retrieval corpus.

Loads opera_corpus.index + opera_corpus.meta, encodes a query with
BGE-M3, and prints the top-5 documents.

Requirements:
    pip install faiss-cpu FlagEmbedding numpy
    # (or faiss-gpu if you have a CUDA build that matches your stack)
"""

from __future__ import annotations

import pickle
from pathlib import Path

import faiss
import numpy as np
from FlagEmbedding import BGEM3FlagModel

HERE = Path(__file__).resolve().parent
INDEX_PATH = HERE / "opera_corpus.index"
META_PATH = HERE / "opera_corpus.meta"

BGE_MODEL = "BAAI/bge-m3"


def main(query: str = "Who directed the film The Big Short?", top_k: int = 5) -> None:
    print(f"loading FAISS index from {INDEX_PATH}")
    index = faiss.read_index(str(INDEX_PATH))

    print(f"loading metadata from {META_PATH}")
    with open(META_PATH, "rb") as f:
        meta = pickle.load(f)

    assert index.ntotal == len(meta), (
        f"count mismatch: index={index.ntotal} meta={len(meta)}"
    )
    print(f"corpus size: {index.ntotal}")

    print(f"loading BGE-M3 ({BGE_MODEL})")
    model = BGEM3FlagModel(BGE_MODEL, use_fp16=True)

    print(f"encoding query: {query!r}")
    encoded = model.encode([query], return_dense=True, return_sparse=False, return_colbert_vecs=False)
    vec = np.asarray(encoded["dense_vecs"], dtype="float32")
    vec /= np.linalg.norm(vec, axis=1, keepdims=True) + 1e-12

    print(f"searching top-{top_k}")
    scores, indices = index.search(vec, top_k)

    for rank, (idx, score) in enumerate(zip(indices[0], scores[0]), start=1):
        rec = meta[int(idx)]
        snippet = rec["content"][:200].replace("\n", " ")
        print(f"  #{rank}  score={score:.4f}  {rec['title']!r}")
        print(f"        {snippet}")


if __name__ == "__main__":
    main()