Qdrant questions are easy to answer from stale memory or from patterns borrowed from a different vector database. Use this skill to ground answers in the official Qdrant documentation and return the closest authoritative page instead of generic vector-search advice.

When to Use

Use this skill when the request is about:

Qdrant concepts: collections, points, vectors, payloads, and named vectors
Search, filtering, hybrid queries, recommendation, and discovery API behavior
Indexing: HNSW parameters, payload indexes, sparse vectors, and quantization (scalar, product, binary)
Storage, snapshots, backups, and the write-ahead log
Distributed deployment, sharding, replication, and consistency guarantees
Multitenancy and payload-based partitioning
Client libraries (Python, JavaScript/TypeScript, Go, Rust, Java, .NET) and the REST/gRPC APIs
FastEmbed, built-in inference, and hybrid text search (BM25 plus dense vectors)
Qdrant Cloud provisioning, RBAC, and Qdrant Edge/on-device deployments
Security: API keys, TLS, and role-based access control

Do not use this skill for:

qdrant-docs

When to Use