Qdrant
- Category
- Databases and data
- Docs outline
- 13 sections
Official docs outline
qdrant.tech- Overview
What Qdrant is and its core building blocks.
- Collections
Creating collections, distance metrics, named vectors and collection config.
- Points
Points, IDs, upserting, batching and retrieval.
- Vectors
Dense, sparse, multivectors and named vector configuration.
- Payload
Attaching JSON payloads to points and payload indexing.
- Search
Nearest neighbor search, scoring, batch search and search parameters.
- Filtering
Conditions, must/should/must_not clauses and payload-based filters.
- Hybrid and Multi-stage Queries
Query API, fusion, prefetch and reranking.
- Indexing and HNSW
HNSW, payload and sparse indexes and index tuning.
- Quantization
Scalar, product and binary quantization to cut memory.
- Distributed Deployment
Sharding, replication and cluster operation.
- Security
API keys, JWT access control and TLS.
- Optimization and Snapshots
Tuning for speed, memory and precision, plus snapshots and backups.
Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.
