Manage Qdrant vector database collections, points, filters, and snapshots via REST API.
npx skills add https://github.com/bug-ops/zeph --skill qdrant-bug-ops
Please help me install this Agent Skill. Skill: qdrant Source: https://github.com/bug-ops/zeph/tree/main/.zeph/skills/qdrant Command: npx skills add https://github.com/bug-ops/zeph --skill qdrant-bug-ops
Managing and querying a Qdrant vector database over REST can be complex without a unified interface. This skill provides a centralized way to perform collection CRUD, vector upserts, searches, and operational tasks like snapshots and cluster status through simple REST calls.
Connect to your Qdrant instance at http://localhost:6333 (override with QDRANT_URL) and begin issuing REST API calls to manage collections, points, filters, and snapshots.
High-intent search queries and answers about installing and using this skill.
You can perform collection CRUD operations on a Qdrant vector database by issuing REST API calls to create, delete, and inspect collections to organize your embeddings.
To upsert points with vectors and payloads in Qdrant, use REST calls to perform similarity searches with optional payload filtering, applying advanced filters like must, should, and must_not to refine results.
Yes, this skill requires a running Qdrant instance accessible by default at http://localhost:6333, which you can override using the QDRANT_URL environment variable, along with curl installed.
Yes, you can manage snapshots, aliases, and monitor cluster health for Qdrant collections through REST API calls to keep production deployments reliable and maintain operational stability.
Yes, the Qdrant REST API supports named vectors and multi-vector queries, allowing you to apply payload indexes to refine results and build a scalable semantic search service.
The best way to build a scalable semantic search service is to store embeddings in Qdrant and use REST API calls to manage collections, upsert points, and serve accurate filtered similarity searches at scale.
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