"""Minimal Qdrant REST client for the ingest pipeline (direct to QDRANT_URL).

Creates the crm_chunks collection per EMBEDDINGS.md: a named dense vector
(1024, Cosine) + a named sparse vector with modifier:idf, plus payload indexes.
"""
import config
import http_util

Q = config.QDRANT_URL
COL = config.COLLECTION


def _req(method, path, body=None):
    return http_util.request(method, f"{Q}{path}", body, verify=False)


def exists() -> bool:
    status, _ = _req("GET", f"/collections/{COL}")
    return status == 200


def create_collection(recreate=False, dim=config.DENSE_DIM):
    if exists():
        if not recreate:
            return "exists"
        _req("DELETE", f"/collections/{COL}")
    status, data = _req("PUT", f"/collections/{COL}", {
        "vectors": {"dense": {"size": dim, "distance": "Cosine"}},
        "sparse_vectors": {"sparse": {"modifier": "idf"}},
    })
    if status not in (200, 201):
        raise RuntimeError(f"create collection -> {status}: {data}")
    return "created"


def ensure_indexes():
    for field, schema in (("lp_id", "keyword"), ("doc_type", "keyword"), ("date_ts", "integer")):
        _req("PUT", f"/collections/{COL}/index", {"field_name": field, "field_schema": schema})


def upsert(points):
    status, data = _req("PUT", f"/collections/{COL}/points?wait=true", {"points": points})
    if status not in (200, 201):
        raise RuntimeError(f"upsert -> {status}: {data}")
    return data


def count():
    status, data = _req("POST", f"/collections/{COL}/points/count", {"exact": True})
    return (data or {}).get("result", {}).get("count")