#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ app/core/qdrant_points.py - robust points helpers for Qdrant - Single source of truth for building PointStruct for notes/chunks/edges - Backward-compatible payloads for edges - Handles both Single-Vector and Named-Vector collections - Deterministic overrides via ENV to avoid auto-detection traps: * NOTES_VECTOR_NAME, CHUNKS_VECTOR_NAME, EDGES_VECTOR_NAME * MINDNET_VECTOR_NAME (fallback) > Set to a concrete name (e.g. "text") to force Named-Vector with that name > Set to "__single__" (or "single") to force Single-Vector Version: 1.5.0 (2025-11-08) """ from __future__ import annotations import os import uuid from typing import List, Tuple, Iterable, Optional, Dict, Any from qdrant_client.http import models as rest from qdrant_client import QdrantClient # --------------------- ID helpers --------------------- def _to_uuid(stable_key: str) -> str: return str(uuid.uuid5(uuid.NAMESPACE_URL, stable_key)) def _names(prefix: str) -> Tuple[str, str, str]: return f"{prefix}_notes", f"{prefix}_chunks", f"{prefix}_edges" # --------------------- Points builders --------------------- def points_for_note(prefix: str, note_payload: dict, note_vec: List[float] | None, dim: int) -> Tuple[str, List[rest.PointStruct]]: notes_col, _, _ = _names(prefix) vector = note_vec if note_vec is not None else [0.0] * int(dim) raw_note_id = note_payload.get("note_id") or note_payload.get("id") or "missing-note-id" point_id = _to_uuid(raw_note_id) pt = rest.PointStruct(id=point_id, vector=vector, payload=note_payload) return notes_col, [pt] def points_for_chunks(prefix: str, chunk_payloads: List[dict], vectors: List[List[float]]) -> Tuple[str, List[rest.PointStruct]]: _, chunks_col, _ = _names(prefix) points: List[rest.PointStruct] = [] for i, (pl, vec) in enumerate(zip(chunk_payloads, vectors), start=1): chunk_id = pl.get("chunk_id") or pl.get("id") if not chunk_id: note_id = pl.get("note_id") or pl.get("parent_note_id") or "missing-note" chunk_id = f"{note_id}#{i}" pl["chunk_id"] = chunk_id point_id = _to_uuid(chunk_id) points.append(rest.PointStruct(id=point_id, vector=vec, payload=pl)) return chunks_col, points def _normalize_edge_payload(pl: dict) -> dict: kind = pl.get("kind") or pl.get("edge_type") or "edge" source_id = pl.get("source_id") or pl.get("src_id") or "unknown-src" target_id = pl.get("target_id") or pl.get("dst_id") or "unknown-tgt" seq = pl.get("seq") or pl.get("order") or pl.get("index") pl.setdefault("kind", kind) pl.setdefault("source_id", source_id) pl.setdefault("target_id", target_id) if seq is not None and "seq" not in pl: pl["seq"] = seq return pl def points_for_edges(prefix: str, edge_payloads: List[dict]) -> Tuple[str, List[rest.PointStruct]]: _, _, edges_col = _names(prefix) points: List[rest.PointStruct] = [] for raw in edge_payloads: pl = _normalize_edge_payload(raw) edge_id = pl.get("edge_id") if not edge_id: kind = pl.get("kind", "edge") s = pl.get("source_id", "unknown-src") t = pl.get("target_id", "unknown-tgt") seq = pl.get("seq") or "" edge_id = f"{kind}:{s}->{t}#{seq}" pl["edge_id"] = edge_id point_id = _to_uuid(edge_id) points.append(rest.PointStruct(id=point_id, vector=[0.0], payload=pl)) return edges_col, points # --------------------- Vector schema & overrides --------------------- def _preferred_name(candidates: List[str]) -> str: for k in ("text", "default", "embedding", "content"): if k in candidates: return k return sorted(candidates)[0] def _env_override_for_collection(collection: str) -> Optional[str]: """ Returns: - "__single__" to force single-vector - concrete name (str) to force named-vector with that name - None to auto-detect """ base = os.getenv("MINDNET_VECTOR_NAME") if collection.endswith("_notes"): base = os.getenv("NOTES_VECTOR_NAME", base) elif collection.endswith("_chunks"): base = os.getenv("CHUNKS_VECTOR_NAME", base) elif collection.endswith("_edges"): base = os.getenv("EDGES_VECTOR_NAME", base) if not base: return None val = base.strip() if val.lower() in ("__single__", "single"): return "__single__" return val # concrete name def _get_vector_schema(client: QdrantClient, collection_name: str) -> dict: """ Return {"kind": "single", "size": int} or {"kind": "named", "names": [...], "primary": str}. """ try: info = client.get_collection(collection_name=collection_name) vecs = getattr(info, "vectors", None) # Single-vector config if hasattr(vecs, "size") and isinstance(vecs.size, int): return {"kind": "single", "size": vecs.size} # Named-vectors config (dict-like in .config) cfg = getattr(vecs, "config", None) if isinstance(cfg, dict) and cfg: names = list(cfg.keys()) if names: return {"kind": "named", "names": names, "primary": _preferred_name(names)} except Exception: pass return {"kind": "single", "size": None} def _as_named(points: List[rest.PointStruct], name: str) -> List[rest.PointStruct]: out: List[rest.PointStruct] = [] for pt in points: vec = getattr(pt, "vector", None) if isinstance(vec, dict): if name in vec: out.append(pt) else: # take any existing entry; if empty dict fallback to [0.0] fallback_vec = None try: fallback_vec = list(next(iter(vec.values()))) except Exception: fallback_vec = [0.0] out.append(rest.PointStruct(id=pt.id, vector={name: fallback_vec}, payload=pt.payload)) elif vec is not None: out.append(rest.PointStruct(id=pt.id, vector={name: vec}, payload=pt.payload)) else: out.append(pt) return out # --------------------- Qdrant ops --------------------- def upsert_batch(client: QdrantClient, collection: str, points: List[rest.PointStruct]) -> None: if not points: return # 1) ENV overrides come first override = _env_override_for_collection(collection) if override == "__single__": client.upsert(collection_name=collection, points=points, wait=True) return elif isinstance(override, str): client.upsert(collection_name=collection, points=_as_named(points, override), wait=True) return # 2) Auto-detect schema schema = _get_vector_schema(client, collection) if schema.get("kind") == "named": name = schema.get("primary") or _preferred_name(schema.get("names") or []) client.upsert(collection_name=collection, points=_as_named(points, name), wait=True) return # 3) Fallback single-vector client.upsert(collection_name=collection, points=points, wait=True) # --- Optional search helpers --- def _filter_any(field: str, values: Iterable[str]) -> rest.Filter: return rest.Filter(should=[rest.FieldCondition(key=field, match=rest.MatchValue(value=v)) for v in values]) def _merge_filters(*filters: Optional[rest.Filter]) -> Optional[rest.Filter]: fs = [f for f in filters if f is not None] if not fs: return None if len(fs) == 1: return fs[0] must = [] for f in fs: if getattr(f, "must", None): must.extend(f.must) if getattr(f, "should", None): must.append(rest.Filter(should=f.should)) return rest.Filter(must=must) def _filter_from_dict(filters: Optional[Dict[str, Any]]) -> Optional[rest.Filter]: if not filters: return None parts = [] for k, v in filters.items(): if isinstance(v, (list, tuple, set)): parts.append(_filter_any(k, [str(x) for x in v])) else: parts.append(rest.Filter(must=[rest.FieldCondition(key=k, match=rest.MatchValue(value=v))])) return _merge_filters(*parts) def search_chunks_by_vector(client: QdrantClient, prefix: str, vector: List[float], top: int = 10, filters: Optional[Dict[str, Any]] = None) -> List[Tuple[str, float, dict]]: _, chunks_col, _ = _names(prefix) flt = _filter_from_dict(filters) res = client.search(collection_name=chunks_col, query_vector=vector, limit=top, with_payload=True, with_vectors=False, query_filter=flt) out: List[Tuple[str, float, dict]] = [] for r in res: out.append((str(r.id), float(r.score), dict(r.payload or {}))) return out