qdrant

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.

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Install skill "qdrant" with this command: npx skills add itechmeat/llm-code/itechmeat-llm-code-qdrant

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 → 1.17.0)

  • 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).

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.

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

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