#!/usr/bin/env python3
"""
load_qdrant.py — Load embedded marine doc chunks into Qdrant (hybrid).

Collection `marine_docs`:
  - dense  (768-dim, Cosine) from gemini-embedding-2-preview
  - sparse (BM25 via FastEmbed) for keyword search
  - payload indexes: product_id, doc_type, chunk_type, section_path

Storage: local persistent Qdrant at out/qdrant_db by default (no server needed);
pass --url http://localhost:6333 to use a server instead.

Usage:
  python load_qdrant.py --recreate
  python load_qdrant.py --limit 500
"""
import argparse
import json
import time
import uuid
from pathlib import Path

from qdrant_client import QdrantClient, models
from fastembed import SparseTextEmbedding

ROOT = Path(__file__).resolve().parent
OUT = ROOT / "out"
COLLECTION = "marine_docs"
DENSE_DIM = 768
INPUT = OUT / "chunks_embedded.jsonl"
LOCAL_DB = OUT / "qdrant_db"
BATCH = 500

_bm25 = SparseTextEmbedding(model_name="Qdrant/bm25")


def to_sparse(text: str) -> models.SparseVector:
    r = list(_bm25.embed([text]))[0]
    return models.SparseVector(indices=r.indices.tolist(), values=r.values.tolist())


def create_collection(client: QdrantClient, recreate: bool) -> None:
    names = [c.name for c in client.get_collections().collections]
    if COLLECTION in names:
        if recreate:
            client.delete_collection(COLLECTION)
        else:
            print(f"'{COLLECTION}' exists: {client.get_collection(COLLECTION).points_count} pts")
            return
    client.create_collection(
        collection_name=COLLECTION,
        vectors_config={"dense": models.VectorParams(size=DENSE_DIM, distance=models.Distance.COSINE)},
        sparse_vectors_config={"sparse": models.SparseVectorParams(modifier=models.Modifier.IDF)},
    )
    for field, schema in [
        ("product_id", models.PayloadSchemaType.KEYWORD),
        ("doc_type", models.PayloadSchemaType.KEYWORD),
        ("chunk_type", models.PayloadSchemaType.KEYWORD),
        ("section_path", models.PayloadSchemaType.KEYWORD),
    ]:
        client.create_payload_index(collection_name=COLLECTION, field_name=field, field_schema=schema)
    print(f"created '{COLLECTION}' (dense+sparse, 4 payload indexes)")


def load(client: QdrantClient, limit=None) -> None:
    buf, total, skipped, t0 = [], 0, 0, time.time()
    with open(INPUT, encoding="utf-8") as f:
        for i, line in enumerate(f):
            if limit and i >= limit:
                break
            c = json.loads(line)
            emb = c.get("embedding")
            if not emb:
                skipped += 1
                continue
            raw = c.get("text_raw", c.get("text_with_prefix", ""))
            buf.append(models.PointStruct(
                id=str(uuid.uuid5(uuid.NAMESPACE_DNS, c["chunk_id"])),
                vector={"dense": emb, "sparse": to_sparse(raw)},
                payload={
                    "chunk_id": c["chunk_id"], "product_id": c["product_id"],
                    "product_title": c.get("product_title", ""),
                    "doc_id": c["doc_id"], "doc_type": c["doc_type"],
                    "section_path": c.get("section_path", ""),
                    "chunk_type": c.get("chunk_type", "text"),
                    "text_raw": raw[:2500],
                },
            ))
            if len(buf) >= BATCH:
                client.upsert(collection_name=COLLECTION, points=buf)
                total += len(buf)
                print(f"  [{total:,}] {total/(time.time()-t0):.0f} pts/s")
                buf = []
    if buf:
        client.upsert(collection_name=COLLECTION, points=buf)
        total += len(buf)
    print(f"DONE: {total:,} points, skipped {skipped}, total now "
          f"{client.get_collection(COLLECTION).points_count:,}")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--limit", type=int, default=None)
    ap.add_argument("--recreate", action="store_true")
    ap.add_argument("--url", type=str, default=None, help="Qdrant server URL; default = local path mode")
    a = ap.parse_args()
    client = QdrantClient(url=a.url, check_compatibility=False) if a.url else QdrantClient(path=str(LOCAL_DB))
    print(f"Qdrant: {'server '+a.url if a.url else 'local '+str(LOCAL_DB)}")
    create_collection(client, a.recreate)
    load(client, a.limit)


if __name__ == "__main__":
    main()