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Tunable EdgeConfig parameters (on_disk_payload, hnsw_config, optimizers) are now Option, and every tunable resolves through the fallback chain provided -> persisted -> derived from segments -> default when loading an existing shard. Leaving a parameter unspecified keeps the shard as it is; an explicit value overwrites it and existing segments converge to it through the optimizers. vectors/sparse_vectors are excluded from overwrite semantics: an empty map inherits the persisted/segment-derived definitions, a non-empty map is validated for compatibility against the loaded segments (size, distance, multivector, datatype, sparse modifier) and fails the load on mismatch. The derived layer folds over all segments in UUID order instead of taking an arbitrary first segment, so a plain appendable segment (which carries no HNSW parameters) can never mask an indexed segment's actual build parameters. Previously a lost edge_config.json could resolve unspecified HNSW params to compiled-in defaults and silently trigger a full re-index via ConfigMismatchOptimizer. The read-only follower accepts an optional config on open: provided tunables are applied once over the segment-derived config (vectors always come from the segments), and refresh re-derives from segments alone. Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Qdrant Edge
Qdrant Edge is a lightweight, in-process vector search engine designed for embedded devices, autonomous systems, and mobile agents. It enables on-device retrieval with minimal memory footprint, no background services, and optional synchronization with Qdrant Cloud.
For connecting to remote Qdrant instances, use the qdrant-client package instead.
- Website: https://qdrant.tech/edge/
- Documentation: https://qdrant.tech/documentation/edge/
- Examples: https://github.com/qdrant/qdrant/tree/dev/lib/edge/python/examples