app/core/qdrant.py aktualisiert
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# app/core/qdrant.py
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Tuple
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import os
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from dataclasses import dataclass
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from typing import Optional
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as rest
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DEFAULT_DIM = int(os.getenv("VECTOR_DIM", "384"))
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# -------------------------------
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# Konfiguration
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# -------------------------------
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@dataclass
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class QdrantConfig:
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url: str
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api_key: str | None = None
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prefix: str = "mindnet"
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dim: int = DEFAULT_DIM
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api_key: Optional[str]
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prefix: str
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dim: int
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def _collection_names(prefix: str) -> Tuple[str, str, str]:
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"""
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Liefert die standardisierten Collection-Namen für Notes, Chunks und Edges.
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"""
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return f"{prefix}_notes", f"{prefix}_chunks", f"{prefix}_edges"
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@staticmethod
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def from_env() -> "QdrantConfig":
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# URL (bevorzugt) oder Host/Port
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url = os.getenv("QDRANT_URL")
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if not url:
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host = os.getenv("QDRANT_HOST", "127.0.0.1")
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port = int(os.getenv("QDRANT_PORT", "6333"))
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url = f"http://{host}:{port}"
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api_key = os.getenv("QDRANT_API_KEY") or None
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# Collection-Prefix und Vektor-Dimension
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prefix = os.getenv("COLLECTION_PREFIX", "mindnet")
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dim = int(os.getenv("VECTOR_DIM", "384")) # MiniLM-384 by default
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return QdrantConfig(url=url, api_key=api_key, prefix=prefix, dim=dim)
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# -------------------------------
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# Client / Setup
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# -------------------------------
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def get_client(cfg: QdrantConfig) -> QdrantClient:
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"""
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Erstellt einen QdrantClient basierend auf der Config.
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"""
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return QdrantClient(url=cfg.url, api_key=cfg.api_key or None, prefer_grpc=False)
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return QdrantClient(url=cfg.url, api_key=cfg.api_key)
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def ensure_collections(cfg: QdrantConfig) -> Tuple[str, str, str]:
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def ensure_collections(client: QdrantClient, prefix: str, dim: int) -> None:
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"""
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Idempotent: legt {prefix}_notes, {prefix}_chunks, {prefix}_edges an,
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falls sie fehlen, und erzeugt sinnvolle Payload-Indizes.
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Stellt sicher, dass die drei Collections existieren:
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- {prefix}_notes : Vektor-Dim = dim (COSINE)
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- {prefix}_chunks : Vektor-Dim = dim (COSINE)
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- {prefix}_edges : Vektor-Dim = 1 (DOT) <-- Dummy-Vektor, damit der Python-Client kein 'vector' zwingt
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Falls {prefix}_edges bereits vektorlos existiert, wird sie gelöscht und mit 1D neu erstellt.
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"""
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client = get_client(cfg)
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notes, chunks, edges = _collection_names(cfg.prefix)
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notes = f"{prefix}_notes"
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chunks = f"{prefix}_chunks"
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edges = f"{prefix}_edges"
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# Vektorkonfiguration
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note_vec = rest.VectorParams(size=cfg.dim, distance=rest.Distance.COSINE)
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chunk_vec = rest.VectorParams(size=cfg.dim, distance=rest.Distance.COSINE)
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edge_vec = rest.VectorParams(size=1, distance=rest.Distance.COSINE) # Dummy für edges
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def _create_if_missing(name: str, vparam: rest.VectorParams):
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try:
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info = client.get_collection(name)
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if info and info.status == rest.CollectionStatus.GREEN:
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return
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except Exception:
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pass
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# Notes
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if not client.collection_exists(notes):
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client.create_collection(
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collection_name=name,
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vectors_config=rest.VectorsConfig(params=vparam),
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optimizers_config=rest.OptimizersConfigDiff(indexing_threshold=20000),
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on_disk_payload=True,
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collection_name=notes,
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vectors_config=rest.VectorParams(size=dim, distance=rest.Distance.COSINE),
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)
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_create_if_missing(notes, note_vec)
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_create_if_missing(chunks, chunk_vec)
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_create_if_missing(edges, edge_vec)
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# Chunks
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if not client.collection_exists(chunks):
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client.create_collection(
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collection_name=chunks,
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vectors_config=rest.VectorParams(size=dim, distance=rest.Distance.COSINE),
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)
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# Payload-Indizes
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def _ensure_index(name: str, field: str, kind: rest.PayloadSchemaType):
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# Edges: 1D Dummy-Vektor (Workaround)
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recreate_edges = False
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if client.collection_exists(edges):
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try:
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client.create_payload_index(
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collection_name=name,
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field_name=field,
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field_schema=rest.PayloadSchemaParams(schema=kind),
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)
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info = client.get_collection(edges)
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# Prüfen, ob Vektor-Konfig existiert
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vectors_cfg = getattr(getattr(info.result, "config", None), "params", None)
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# Neuere Clients: info.result.config.params.vectors kann VectorParams oder dict sein
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has_vectors = getattr(vectors_cfg, "vectors", None) is not None
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if not has_vectors:
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recreate_edges = True
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except Exception:
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# Index existiert evtl. schon → ignorieren
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pass
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# Wenn Metadaten nicht lesbar → sicherheitshalber neu anlegen
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recreate_edges = True
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else:
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# Existiert noch nicht → wird gleich erstellt
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pass
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# Notes-Collection
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for f in ("note_id", "type", "status", "project", "area", "path", "tags"):
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_ensure_index(notes, f, rest.PayloadSchemaType.KEYWORD)
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if recreate_edges and client.collection_exists(edges):
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client.delete_collection(edges)
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# Chunks-Collection
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for f in ("note_id", "type", "tags", "section_title", "section_path", "path", "chunk_index"):
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_ensure_index(chunks, f, rest.PayloadSchemaType.KEYWORD)
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if not client.collection_exists(edges):
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client.create_collection(
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collection_name=edges,
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vectors_config=rest.VectorParams(size=1, distance=rest.Distance.DOT),
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)
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# Edges-Collection
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for f in ("src_id", "dst_id", "edge_type", "scope"):
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_ensure_index(edges, f, rest.PayloadSchemaType.KEYWORD)
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return notes, chunks, edges
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# -------------------------------
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# (Optionale) Utility-Funktionen
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# -------------------------------
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def collection_names(prefix: str) -> tuple[str, str, str]:
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"""Hilfsfunktion, falls du die Namen an einer Stelle brauchst."""
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return (f"{prefix}_notes", f"{prefix}_chunks", f"{prefix}_edges")
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def wipe_collections(client: QdrantClient, prefix: str) -> None:
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"""
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Löscht alle drei Collections (nur nutzen, wenn du bewusst neu aufsetzen willst).
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"""
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for name in collection_names(prefix):
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if client.collection_exists(name):
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client.delete_collection(name)
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