qdrant.tech/documentation

Paginate Qdrant results with the scroll API and run cheap-then-precise multi-stage rescoring with prefetch and quantization oversampling

Run Qdrant recommend, discover, and context queries through the Query API using positive/negative examples

Deploy a Qdrant collection across a cluster with sharding and replication, tune read/write consistency, and manage shards via the cluster API

Update or delete Qdrant point payloads without touching vectors, and reclaim disk after mass deletes

Bulk load points into Qdrant efficiently with batch upsert and indexing disabled during ingestion

Bulk load points into Qdrant efficiently with batch upsert and indexing disabled during ingestion

Set up a Qdrant Cloud cluster and authenticate over REST and gRPC with database API keys

Use Qdrant collection aliases for zero-downtime reindexing when switching to a new embedding model

Implement Qdrant multitenancy by partitioning one collection with a tenant payload field, is_tenant indexing, and optional custom shard-key routing

Create, download, and restore Qdrant snapshots (collection, shard, and full-storage) for backup and migration

Create Qdrant payload field indexes (keyword, integer, float, bool, datetime, uuid, full-text, geo) with correct tokenizer and on-disk settings

Build a filtered vector search in Qdrant using must/should/must_not/min_should with payload indexes so filters do not silently fall back to full scans

Combine dense and sparse vectors in one Qdrant collection and run hybrid search with prefetch + RRF/DBSF fusion via the Query API

Select and configure Qdrant quantization (scalar, binary, or product) for a collection and enable rescoring

Tune Qdrant collection HNSW graph parameters and enable on-disk payload indexing for large collections

Qdrant: create a collection and perform a vector search