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99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""
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Erzeugt Qdrant-Collections für das mindnet-Projekt:
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- mindnet_chunks : semantische Suche über Markdown-Text-Chunks (384/Cosine)
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- mindnet_notes : 1 Punkt pro Notiz (Metadaten, optional Titel-Embedding)
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- mindnet_edges : explizite Link-Kanten (Dummy-Vektor size=1; Filter über Payload)
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Idempotent: legt nur an, wenn nicht vorhanden. Legt sinnvolle Payload-Indizes an.
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"""
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import os
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import sys
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import json
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import argparse
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import requests
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DEFAULT_QDRANT_URL = os.environ.get("QDRANT_URL", "http://127.0.0.1:6333")
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def rq(method: str, path: str, **kwargs) -> requests.Response:
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url = DEFAULT_QDRANT_URL.rstrip("/") + path
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r = requests.request(method, url, timeout=15, **kwargs)
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if not r.ok:
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raise RuntimeError(f"{method} {url} -> {r.status_code} {r.text}")
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return r
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def collection_exists(name: str) -> bool:
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r = rq("GET", f"/collections/{name}")
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data = r.json()
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return data.get("result", {}).get("status") == "green"
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def create_collection(name: str, size: int, distance: str = "Cosine") -> None:
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if collection_exists(name):
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print(f"[=] Collection '{name}' existiert bereits – überspringe Anlage.")
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return
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payload = {"vectors": {"size": size, "distance": distance}}
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rq("PUT", f"/collections/{name}", json=payload)
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print(f"[+] Collection '{name}' angelegt (size={size}, distance={distance}).")
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def create_keyword_index(collection: str, field: str) -> None:
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payload = {"field_name": field, "field_schema": "keyword"}
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rq("PUT", f"/collections/{collection}/index", json=payload)
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print(f"[+] Index keyword on {collection}.{field}")
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def create_text_index(collection: str, field: str = "text") -> None:
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payload = {"field_name": field, "field_schema": {"type": "text"}}
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rq("PUT", f"/collections/{collection}/index", json=payload)
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print(f"[+] Index text on {collection}.{field}")
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--qdrant-url", default=DEFAULT_QDRANT_URL, help="z.B. http://127.0.0.1:6333")
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ap.add_argument("--prefix", default="mindnet", help="Collection-Präfix (default: mindnet)")
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ap.add_argument("--dim", type=int, default=384, help="Embedding-Dimension (384 für all-MiniLM-L6-v2)")
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ap.add_argument("--distance", default="Cosine", choices=["Cosine", "Euclid", "Dot"], help="Distanzmetrik")
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args = ap.parse_args()
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global DEFAULT_QDRANT_URL
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DEFAULT_QDRANT_URL = args.qdrant_url
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chunks = f"{args.prefix}_chunks"
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notes = f"{args.prefix}_notes"
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edges = f"{args.prefix}_edges"
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# 1) Collections anlegen
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create_collection(chunks, size=args.dim, distance=args.distance)
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create_collection(notes, size=args.dim, distance=args.distance)
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create_collection(edges, size=1, distance=args.distance) # Dummy-Vektor
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# 2) Indizes definieren
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# mindnet_chunks: häufige Filter + Volltext
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for f in ["note_id", "Status", "Typ", "title", "path"]:
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create_keyword_index(chunks, f)
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for f in ["tags", "Rolle", "links"]:
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create_keyword_index(chunks, f)
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create_text_index(chunks, "text") # Wort-/Phrasensuche
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# mindnet_notes: Metadaten der Notizen
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for f in ["note_id", "title", "path", "Typ", "Status"]:
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create_keyword_index(notes, f)
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for f in ["tags", "Rolle"]:
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create_keyword_index(notes, f)
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# mindnet_edges: Graph/Kanten (Filter-only)
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for f in ["src_note_id", "dst_note_id", "src_chunk_id", "dst_chunk_id", "link_text", "relation"]:
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create_keyword_index(edges, f)
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# 3) Ausgabe
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r = rq("GET", "/collections")
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print("\n[Info] Collections vorhanden:")
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print(json.dumps(r.json().get("result", {}).get("collections", []), indent=2, ensure_ascii=False))
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"[ERROR] {e}", file=sys.stderr)
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sys.exit(1)
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