scripts/marine_rag/load_copernicus_docs.py

#!/usr/bin/env python3
"""
load_copernicus_docs.py — build the unified `copernicus_docs` Qdrant collection:
one metadata card per dataset across ALL four Copernicus stores (~1415).

  - 164 CDS/ADS/EWDS cards from out/cds_cards_embedded.jsonl  (store=CDS/ADS/EWDS)
  - 1251 Marine dataset cards, reusing the already-embedded CARD chunks in
    out/chunks_embedded.jsonl                                  (store=CMEMS)

Dense (768-dim Cosine, gemini-embedding-2-preview) + sparse (BM25) hybrid,
same recipe as marine_docs. Adds a `store` payload keyword for per-store filtering.
The deep Marine PUM/QUID/SQO doc-RAG (marine_docs) is left untouched.
"""
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 = "copernicus_docs"
DENSE_DIM = 768
LOCAL_DB = OUT / "qdrant_db"
CDS_EMB = OUT / "cds_cards_embedded.jsonl"
MARINE_EMB = OUT / "chunks_embedded.jsonl"
BATCH = 400

_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 iter_cards():
    """Yield (chunk, store) for every dataset card to index."""
    # CDS/ADS/EWDS — each row already carries a `store` field
    for line in open(CDS_EMB, encoding="utf-8"):
        c = json.loads(line)
        if c.get("embedding"):
            yield c, c.get("store", "CDS")
    # Marine — reuse CARD chunks only, tag store=CMEMS
    seen = set()
    for line in open(MARINE_EMB, encoding="utf-8"):
        c = json.loads(line)
        if c.get("doc_type") != "CARD" or not c.get("embedding"):
            continue
        if c["chunk_id"] in seen:
            continue
        seen.add(c["chunk_id"])
        yield c, "CMEMS"


def create_collection(client: QdrantClient) -> None:
    names = [c.name for c in client.get_collections().collections]
    if COLLECTION in names:
        client.delete_collection(COLLECTION)
    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 in ("product_id", "doc_type", "store"):
        client.create_payload_index(collection_name=COLLECTION, field_name=field,
                                    field_schema=models.PayloadSchemaType.KEYWORD)
    print(f"created '{COLLECTION}' (dense+sparse, 3 payload indexes)")


def load(client: QdrantClient) -> None:
    buf, total, t0 = [], 0, time.time()
    per_store = {}
    for c, store in iter_cards():
        per_store[store] = per_store.get(store, 0) + 1
        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": c["embedding"], "sparse": to_sparse(raw)},
            payload={
                "chunk_id": c["chunk_id"],
                "product_id": c["product_id"],
                "product_title": c.get("product_title", ""),
                "dataset_id": c.get("doc_id", c["product_id"]),
                "doc_type": c.get("doc_type", "CARD"),
                "chunk_type": c.get("chunk_type", "card"),
                "store": store,
                "text_raw": raw[:2500],
            },
        ))
        if len(buf) >= BATCH:
            client.upsert(collection_name=COLLECTION, points=buf)
            total += len(buf); buf = []
            print(f"  [{total:,}] {total/(time.time()-t0):.0f} pts/s")
    if buf:
        client.upsert(collection_name=COLLECTION, points=buf)
        total += len(buf)
    print(f"DONE: {total:,} points | per store: {per_store} | "
          f"collection now {client.get_collection(COLLECTION).points_count:,}")


def main():
    client = QdrantClient(path=str(LOCAL_DB))
    print(f"Qdrant local: {LOCAL_DB}")
    create_collection(client)
    load(client)


if __name__ == "__main__":
    main()