ops-qdrant

# ops-qdrant Read-only health detection for the Qdrant vector store and the semantic-recall path that depends on it. It answers three questions without mutating anything: 1. Is Qdrant up, and which collections exist? 2. Do the collections we expect exist, and how many points does each hold? 3. Does end-to-end **dense** (semantic) recall actually work through the memory daemon, or has it silently degraded to BM25-only (`dense == 0.0`)? ## Boundary (non-negotiable) This skill NEVER writes to Qdrant or Arango. Per `memory/SKILL.md`, all AQL and all Qdrant mutation/sync logic live ONLY in `~/workspace/experiments/memory`. ops-qdrant detects and reports; remediation is triggered by `ops-memory` calling the memory repo's sanctioned migration (`migrate_arango_embeddings_to_qdrant.py`) and daemon endpoints. If you find a gap here, do not "fix" it from this skill. ## Commands ```bash # Full read-only health report (human-readable) ./run.sh check # Same as JSON for monitors / ops-memory composition ./run.sh check --json # Just list collections + point counts ./run.sh collections # Just the end-to-end dense-recall probe through the daemon ./run.sh dense-probe --q "memory first recall pattern" # Flag expected collections that are absent (comma-separated, or via env) ./run.sh check --expect memory_chunks_mm_jina_v5_omni_small_1024,persona_memory # Assess external code for Qdrant single-owner boundary violations ./run.sh assess path/to/skill_script.py [--json] ``` ## assess — Qdrant single-owner boundary linter Qdrant is a **single-owner** store: the memory repo (`graph_memory`) owns all collection config and semantic sync (best-practices-arangodb rule 22b). `assess` flags external code that breaks that boundary — a raw `from qdrant_client import` (the PyPI library, *not* the sanctioned `graph_memory.qdrant_client` wrapper) or a Qdrant `create_collection`/`recreate_collection`/`delete_collection` call in a skill. It emits `ops_qdrant.assess.v1` and exits `1` on any violation, so CI can gate changed files: ```bash git diff --name-only main | grep '\.py$' | while read f; do ./run.sh assess "$f"; done ``` ## Environment | Var | Default | Meaning | |-----|---------|---------| | `QDRANT_URL` | `http://127.0.0.1:6333` | Qdrant REST endpoint | | `MEMORY_URL` | `http://127.0.0.1:8601` | memory daemon (owns `/recall`) | | `EMBED_URL` | `http://127.0.0.1:8603` | published jina embedder health | | `OPS_QDRANT_EXPECT` | (unset) | comma-separated collections that must exist | ## Signals - `qdrant_up=false` → hard failure; Qdrant unreachable. Exit 2. - `missing_expected` non-empty → an expected collection is absent from Qdrant. - `dense_max == 0.0` with `found=true` → **semantic recall degraded to BM25-only** (the `graph-memory-operator#145` condition). Per `memory/SKILL.md`, `dense=0.0` means Qdrant semantic recall is unavailable — check `embry-embedding-mm`, `embry-qdrant`, and `qdrant_point_id` metadata. - `embedder_up=false` → the daemon cannot compute query vectors. ## JSON shape ```json { "schema": "ops_qdrant.health.v1", "qdrant_up": true, "embedder_up": true, "memory_daemon_up": true, "collections": [{"name": "...", "points": 12345}], "missing_expected": ["skill_chains"], "dense_probe": {"q": "...", "found": true, "dense_max": 0.0, "dense_ok": false}, "status": "degraded", "warnings": ["dense recall is 0.0 (BM25-only); Qdrant semantic recall unavailable"] } ``` `status` is `healthy`, `degraded`, or `down`. Exit code: 0 healthy/degraded (report written), 2 when Qdrant itself is unreachable. ## References (retrieve on demand — do not vendor) External docs drift; cite the canonical URLs and fetch them when needed with `/context7` (library docs) or `/fetcher` (any URL/PDF) rather than caching stale copies. All four were reachable (HTTP 200) as of 2026-08-24. - Qdrant docs (concepts, collections, vectors): <https://qdrant.tech/documentation/> - Qdrant collection-info API (points_count, named vectors): <https://api.qdrant.tech/api-reference/collections/get-collection> - jina-embeddings-v4 model card: <https://jina.ai/models/jina-embeddings-v4/> - jina-embeddings-v4 on Hugging Face: <https://huggingface.co/jinaai/jina-embeddings-v4> - jina-embeddings-v4 paper (unified text+image, single/multi-vector): <https://arxiv.org/abs/2506.18902> Retrieval examples: ```bash # Library-doc lookup for Qdrant API shapes skills/context7/run.sh "qdrant collection info named vectors points_count" # Fetch a specific page/PDF for offline reading in this turn skills/fetcher/run.sh "https://api.qdrant.tech/api-reference/collections/get-collection" ```