Qdrant edge

Building on Qdrant Edge

Edge is the Qdrant engine embedded in your process (Python or Rust), not a thin local vector store to wrap. The failure mode is rebuilding what the shard already ships: keyword scoring, snapshot apply, faceting, counting. Before writing any of that, check the shard API. Two things Edge does NOT give you are a one-call cloud sync and query-time fusion, so knowing which is which keeps you from both reinventing built-ins and expecting capabilities Edge lacks. Edge is single-node and shares the server's data format.

• Edge is in beta: pin your version, the API drifts between releases Qdrant Edge.

Syncing a Shard with a Qdrant Server

Use when: seeding a shard from a server, keeping it fresh, backing it up, or aggregating many devices into one collection.

There is no built-in .sync(). Sync is a pattern you assemble from shard helpers plus your own transport, so do not go looking for one call.

• Follow the documented dual-shard pattern: a mutable shard for local writes plus an immutable shard restored from a server snapshot, query both, refresh on a schedule Edge synchronization guide.
• You write the snapshot download (plain HTTP to the shard snapshot endpoint), then apply it with unpack_snapshot and update_from_snapshot. Do not untar or merge segments by hand Synchronization patterns.
• Refresh incrementally with a partial snapshot built from snapshot_manifest, not a full snapshot every cycle Synchronization patterns.
• Push is your own dual-write: on each local upsert, enqueue the point and let a background worker upsert it to the server, buffering while offline Synchronization patterns.

Keyword and Hybrid Search on Device

Use when: you need exact-term or BM25 matching, alone or alongside vectors.

• BM25 is built into Edge (Bm25, Bm25Config, embed_document, embed_query) with the IDF Modifier on EdgeSparseVectorParams, and is wire-compatible with server BM25: a shard seeded from a server snapshot answers local BM25 queries without re-indexing. Do not ship a second BM25 library Edge BM25
• Dense embeddings are NOT in Edge: generate them on device with the separate fastembed package FastEmbed embeddings
• Edge queries one vector field per request (using) and does not fuse dense and sparse at query time. Run each leg separately and combine the rankings in application code Edge quickstart

Operating the Shard

Use when: writes have accumulated, search looks stale after inserts, or a backup is larger than the data.

• Edge has NO background optimizer. Call optimize after bulk writes: it builds indexes (including the sparse index) and reclaims deleted points. Skip it and that data stays unindexed Edge quickstart
• Faceting, counting, and enumeration are built in (facet, count, scroll); index the fields you filter or facet with create_field_index rather than aggregating in application code Edge quickstart
• The write-ahead log is pre-allocated to 32 MB and inflates apparent disk and backup size. Shrink it with wal_options (Rust), and do not treat raw file size as real usage Edge quickstart

What NOT to Do

• Expect a bidirectional .sync() or a built-in push path: Edge gives you snapshot apply, you own the transport and the dual-write
• Untar or merge snapshot segments by hand instead of using unpack_snapshot and update_from_snapshot
• Ship a custom or third-party BM25 when Edge has one built in
• Use embed_document for queries or embed_query for documents: the weighting differs and results go wrong
• Assume Edge fuses dense and sparse or consumes Prefetch: combine the rankings in application code
• Assume a background optimizer like the server's: nothing is indexed or compacted until you call optimize
• Reach for Edge when you need distributed or multi-node search: it is single-node Qdrant Edge
• Claim support for a language beyond Python and Rust, or an OS or accelerator the Edge docs do not state