mindnet/app/core/qdrant.py

# app/core/qdrant.py
from __future__ import annotations

import os
from dataclasses import dataclass
from typing import Optional

from qdrant_client import QdrantClient
from qdrant_client.http import models as rest


# -------------------------------
# Konfiguration
# -------------------------------

@dataclass
class QdrantConfig:
    url: str
    api_key: Optional[str]
    prefix: str
    dim: int

    @staticmethod
    def from_env() -> "QdrantConfig":
        # URL (bevorzugt) oder Host/Port
        url = os.getenv("QDRANT_URL")
        if not url:
            host = os.getenv("QDRANT_HOST", "127.0.0.1")
            port = int(os.getenv("QDRANT_PORT", "6333"))
            url = f"http://{host}:{port}"
        api_key = os.getenv("QDRANT_API_KEY") or None

        # Collection-Prefix und Vektor-Dimension
        prefix = os.getenv("COLLECTION_PREFIX", "mindnet")
        dim = int(os.getenv("VECTOR_DIM", "384"))  # MiniLM-384 by default

        return QdrantConfig(url=url, api_key=api_key, prefix=prefix, dim=dim)


# -------------------------------
# Client / Setup
# -------------------------------

def get_client(cfg: QdrantConfig) -> QdrantClient:
    """
    Erstellt einen QdrantClient basierend auf der Config.
    """
    return QdrantClient(url=cfg.url, api_key=cfg.api_key)


def ensure_collections(client: QdrantClient, prefix: str, dim: int) -> None:
    """
    Stellt sicher, dass die drei Collections existieren:
      - {prefix}_notes   : Vektor-Dim = dim (COSINE)
      - {prefix}_chunks  : Vektor-Dim = dim (COSINE)
      - {prefix}_edges   : Vektor-Dim = 1 (DOT)  <-- Dummy-Vektor, damit der Python-Client kein 'vector' zwingt
    Falls {prefix}_edges bereits vektorlos existiert, wird sie gelöscht und mit 1D neu erstellt.
    """
    notes = f"{prefix}_notes"
    chunks = f"{prefix}_chunks"
    edges = f"{prefix}_edges"

    # Notes
    if not client.collection_exists(notes):
        client.create_collection(
            collection_name=notes,
            vectors_config=rest.VectorParams(size=dim, distance=rest.Distance.COSINE),
        )

    # Chunks
    if not client.collection_exists(chunks):
        client.create_collection(
            collection_name=chunks,
            vectors_config=rest.VectorParams(size=dim, distance=rest.Distance.COSINE),
        )

    # Edges: 1D Dummy-Vektor (Workaround)
    recreate_edges = False
    if client.collection_exists(edges):
        try:
            info = client.get_collection(edges)
            # Prüfen, ob Vektor-Konfig existiert
            vectors_cfg = getattr(getattr(info.result, "config", None), "params", None)
            # Neuere Clients: info.result.config.params.vectors kann VectorParams oder dict sein
            has_vectors = getattr(vectors_cfg, "vectors", None) is not None
            if not has_vectors:
                recreate_edges = True
        except Exception:
            # Wenn Metadaten nicht lesbar → sicherheitshalber neu anlegen
            recreate_edges = True
    else:
        # Existiert noch nicht → wird gleich erstellt
        pass

    if recreate_edges and client.collection_exists(edges):
        client.delete_collection(edges)

    if not client.collection_exists(edges):
        client.create_collection(
            collection_name=edges,
            vectors_config=rest.VectorParams(size=1, distance=rest.Distance.DOT),
        )


# -------------------------------
# (Optionale) Utility-Funktionen
# -------------------------------

def collection_names(prefix: str) -> tuple[str, str, str]:
    """Hilfsfunktion, falls du die Namen an einer Stelle brauchst."""
    return (f"{prefix}_notes", f"{prefix}_chunks", f"{prefix}_edges")


def wipe_collections(client: QdrantClient, prefix: str) -> None:
    """
    Löscht alle drei Collections (nur nutzen, wenn du bewusst neu aufsetzen willst).
    """
    for name in collection_names(prefix):
        if client.collection_exists(name):
            client.delete_collection(name)