mindnet/scripts/setup_mindnet_collections.py

#!/usr/bin/env python3
"""
Richtet die Qdrant-Collections für das mindnet-Projekt ein (V2).

- mindnet_chunks : semantische Suche über Text-Chunks (384/Cosine)
- mindnet_notes  : 1 Punkt pro Notiz (optional Titel-Embedding)
- mindnet_edges  : explizite Link-Kanten (Dummy-Vektor size=1; Filter über Payload)

Idempotent: legt nur an, wenn nicht vorhanden.
"""

import os
import sys
import json
import argparse
import requests

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

def rq(method: str, path: str, **kwargs) -> requests.Response:
    url = DEFAULT_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 collection_exists(name: str) -> bool:
    r = rq("GET", f"/collections/{name}")
    data = r.json()
    return data.get("result", {}).get("status") == "green"

def create_collection(name: str, size: int, distance: str = "Cosine") -> None:
    if collection_exists(name):
        print(f"[=] Collection '{name}' existiert bereits – überspringe Anlage.")
        return
    payload = {"vectors": {"size": size, "distance": distance}}
    rq("PUT", f"/collections/{name}", json=payload)
    print(f"[+] Collection '{name}' angelegt (size={size}, distance={distance}).")

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

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

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--qdrant-url", default=DEFAULT_QDRANT_URL, help="z.B. http://127.0.0.1:6333")
    ap.add_argument("--prefix", default="mindnet", help="Collection-Präfix (default: mindnet)")
    ap.add_argument("--dim", type=int, default=384, help="Embedding-Dimension (384 für all-MiniLM-L6-v2)")
    ap.add_argument("--distance", default="Cosine", choices=["Cosine", "Euclid", "Dot"], help="Distanzmetrik")
    args = ap.parse_args()

    # Hier brauchen wir KEIN global, wir überschreiben einfach die Variable lokal
    qdrant_url = args.qdrant_url

    # Hilfsfunktion neu binden
    def rq(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

    # Ab hier wie gehabt
    chunks = f"{args.prefix}_chunks"
    notes  = f"{args.prefix}_notes"
    edges  = f"{args.prefix}_edges"

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

    # 2) Indizes setzen
    for f in ["note_id", "Status", "Typ", "title", "path"]:
        create_keyword_index(chunks, f)
    for f in ["tags", "Rolle", "links"]:
        create_keyword_index(chunks, f)
    create_text_index(chunks, "text")

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

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

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