| name | qdrant |
| description | Qdrant vector database: collections, points, payload filtering, indexing, quantization, snapshots, and Docker/Kubernetes deployment. Use when managing Qdrant collections, performing vector searches with payload filters, configuring HNSW indexes or quantization, or deploying Qdrant clusters. Keywords: Qdrant, vector database, HNSW, quantization, semantic search. |
| metadata | {"version":"1.18.0","release_date":"2026-05-11"} |
Qdrant (Skill Router)
This file is intentionally introductory.
It acts as a router: based on your situation, open the right note under references/.
Release Highlights (1.16.3 → 1.18.0)
- Monitoring + ops: new APIs for optimization progress/stages and cluster-wide telemetry, plus a dedicated HTTP port option for
/metrics.
- Security: audit access logging and secondary API key support (rotation).
- Retrieval: relevance feedback and Weighted RRF for hybrid ranking.
- Write semantics:
update_mode for upserts (upsert / update / insert).
- 1.18.0: TurboQuant adds an aggressive vector-compression path, collections can add/delete named vectors in place, and operators get low-memory/strict-memory knobs plus deeper memory reporting.
Breaking / Upgrade Notes (1.17.0)
- gRPC clients: response format for vector fields changed in gRPC. Upgrade official Qdrant client libraries and validate any custom gRPC integrations.
- Storage upgrades: RocksDB is removed in favor of gridstore. If you are on v1.15.x, do not upgrade directly to v1.17.x — upgrade one minor version at a time.
Additional Upgrade Notes (1.18.0)
- Internal gRPC endpoints now enforce API key/JWT authentication when auth is enabled; validate internal service-to-service traffic before upgrade if you previously relied on private-network-only trust.
- Snapshot restore from URL can now be disabled by config, which is relevant for hardened/self-hosted environments.
Start here (fast)
- New to Qdrant? Read:
references/concepts.md.
- Want the fastest local validation? Read:
references/quickstart.md + references/deployment.md.
- Integrating with Python? Read:
references/api-clients.md.
Choose by situation
Data modeling
- What should go into vectors vs payload vs your main DB? Read:
references/modeling.md.
- Working with IDs, upserts, and write semantics? Read:
references/points.md.
- Need to understand payload types and update modes? Read:
references/payload.md.
Retrieval (search)
- One consolidated entry point (search + filtering + explore + hybrid):
references/retrieval.md.
Performance & indexing
- Index types and tradeoffs:
references/indexing.md.
- Storage/optimizer internals that matter operationally:
references/storage.md + references/optimizer.md.
- Practical tuning, monitoring, troubleshooting:
references/ops-checklist.md.
Deployment & ops
- Installation/Docker/Kubernetes:
references/deployment.md.
- Configuration layering:
references/configuration.md.
- Security/auth/TLS boundary:
references/security.md.
- Backup/restore:
references/snapshots.md.
API interface choice
- REST vs gRPC, Python SDK:
references/api-clients.md.
How to maintain this skill
- Keep
SKILL.md short (router + usage guidance).
- Put details into
references/*.md.
- Merge or reorganize references when it improves discoverability.
Critical prohibitions
- Do not ingest/quote large verbatim chunks of vendor docs; summarize in your own words.
- Do not invent defaults not explicitly grounded in documentation; record uncertainties as TODOs.
- Do not design backup/restore without testing a restore path.
- Do not use NFS as the primary persistence backend (installation docs explicitly warn against it).
- Do not expose internal cluster communication ports publicly; rely on private networking.
- Do not use API keys/JWT over untrusted networks without TLS.
- Do not rely on implicit runtime defaults for production; record effective configuration.
Links
