#!/usr/bin/env python3
"""
Richtet Qdrant-Collections für dein Mindnet-Projekt ein (idempotent).

Erzeugt:
- mindnet_chunks  (size=384, distance=Cosine)  -> semantische Suche über Text-Chunks
- mindnet_notes   (size=384, distance=Cosine)  -> Notizebene / Facettierung
- mindnet_edges   (size=1,   distance=Cosine)  -> explizite Links (Dummy-Vektor; Filter via Payload)

Legt sinnvolle Payload-Indizes an (keyword/text).
"""

import os
import sys
import json
import argparse
import requests

QDRANT_URL = os.environ.get("QDRANT_URL", "http://127.0.0.1:6333")

def api(method: str, path: str, **kwargs) -> requests.Response:
    url = QDRANT_URL.rstrip("/") + path
    r = requests.request(method, url, timeout=15, **kwargs)
    if not r.ok:
        raise RuntimeError(f"{method} {url} -> {r.status_code} {r.text}")
    return r

def exists(collection: str) -> bool:
    r = api("GET", f"/collections/{collection}")
    j = r.json()
    return j.get("result", {}).get("status") == "green"

def create_collection(collection: str, size: int, distance: str) -> None:
    if exists(collection):
        print(f"[=] {collection} existiert bereits.")
        return
    payload = {"vectors": {"size": size, "distance": distance}}
    api("PUT", f"/collections/{collection}", json=payload)
    print(f"[+] Collection {collection} angelegt (size={size}, distance={distance}).")

def keyword_index(collection: str, field: str) -> None:
    api("PUT", f"/collections/{collection}/index",
        json={"field_name": field, "field_schema": "keyword"})
    print(f"[+] keyword-Index: {collection}.{field}")

def text_index(collection: str, field: str = "text") -> None:
    api("PUT", f"/collections/{collection}/index",
        json={"field_name": field, "field_schema": {"type": "text"}})
    print(f"[+] text-Index: {collection}.{field}")

def main():
    p = argparse.ArgumentParser()
    p.add_argument("--qdrant-url", default=QDRANT_URL, help="z.B. http://127.0.0.1:6333")
    p.add_argument("--prefix", default="mindnet", help="Präfix für Collections")
    p.add_argument("--dim", type=int, default=384, help="Embedding-Dimension (MiniLM: 384)")
    p.add_argument("--distance", default="Cosine", choices=["Cosine", "Euclid", "Dot"],
                   help="Distanzmetrik")
    args = p.parse_args()

    global QDRANT_URL
    QDRANT_URL = args.qdrant_url

    chunks = f"{args.prefix}_chunks"
    notes  = f"{args.prefix}_notes"
    edges  = f"{args.prefix}_edges"

    # 1) Collections
    create_collection(chunks, args.dim, args.distance)
    create_collection(notes,  args.dim, args.distance)
    create_collection(edges,  1,         args.distance)  # Dummy-Vektor

    # 2) Indizes
    # mindnet_chunks
    for f in ["note_id", "Status", "Typ", "title", "path"]:
        keyword_index(chunks, f)
    for f in ["tags", "Rolle", "links"]:
        keyword_index(chunks, f)
    text_index(chunks, "text")

    # mindnet_notes
    for f in ["note_id", "title", "path", "Typ", "Status"]:
        keyword_index(notes, f)
    for f in ["tags", "Rolle"]:
        keyword_index(notes, f)

    # mindnet_edges
    for f in ["src_note_id", "dst_note_id", "src_chunk_id", "dst_chunk_id", "link_text", "relation"]:
        keyword_index(edges, f)

    # 3) Übersicht
    coll = api("GET", "/collections").json().get("result", {}).get("collections", [])
    print("\n[Info] Collections vorhanden:")
    print(json.dumps(coll, indent=2, ensure_ascii=False))

if __name__ == "__main__":
    try:
        main()
    except Exception as e:
        print(f"[ERROR] {e}", file=sys.stderr)
        sys.exit(1)