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Qdrant Edge

QdrantOfficialPublished by Qdrant

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
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From the author

Guides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with the server', 'keep a local shard in sync with Qdrant Cloud', 'BM25 or keyword search on Edge', 'hybrid search on Edge', 'embeddings on device', 'Edge snapshots', 'apply a partial snapshot', 'why is my Edge search empty after inserts', or is writing custom sync, BM25, or fusion code against qdrant-edge. Also use when deciding what Edge ships built-in versus what you must implement.
— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Qdrant Edge is an in-process vector engine for Python and Rust. This skill provides essential guidance on managing local shards, implementing synchronization patterns between Edge and Qdrant Cloud, and leveraging built-in features like BM25 and hybrid search. It emphasizes avoiding common pitfalls such as manual snapshot manipulation or redundant implementation of features already supported by the engine.

What it does

  • Explains the dual-shard pattern for keeping local data in sync with remote server snapshots.
  • Details how to implement hybrid search using built-in BM25 and fusion capabilities.
  • Provides instructions for manual shard optimization and index management.
  • Clarifies the division of responsibility between the Edge engine and application-level code.
  • Outlines the correct usage of snapshot tools for data updates and backups.

When to use it

  • When designing synchronization logic between local devices and a central Qdrant server.
  • When implementing keyword or hybrid search within an embedded application.
  • When troubleshooting stale search results or performance issues after bulk data inserts.
  • When determining whether a feature is natively supported by Edge or requires custom implementation.

Advantages

  • Prevents redundant development by highlighting built-in engine capabilities.
  • Ensures stable and performant shard operations through recommended maintenance patterns.
  • Provides clear architectural guidance for handling offline-first or edge-computing scenarios.
  • Reduces integration errors by defining the boundaries of the embedded engine's API.

Usage examples

“How do I keep my local Qdrant Edge shard updated with my Qdrant Cloud collection?”
The agent explains the dual-shard pattern, recommending an immutable shard for server data and a mutable shard for local writes, and outlines the process for applying partial snapshots.
“Why are my search results empty after I insert data into Qdrant Edge?”
The agent identifies that Edge lacks a background optimizer and instructs the user to call the optimize method to build indexes and reclaim space.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/qdrant/qdrant-edge/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip qdrant-edge.zip -d .agents/skills

Documentation by Qdrant

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. One thing Edge does NOT give you is a one-call cloud sync, so knowing what is built in 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 the server's sparse vector BM25 model (Edge BM25 docs cover only the sparse vector variant): 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

  • For hybrid search, run the dense and sparse legs as prefetches and fuse them with a Fusion query in a single query call, instead of combining rankings in application code Reading data

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

  • Combine dense and sparse rankings in application code: Edge query accepts prefetches and fuses them with a Fusion query

  • 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

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

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Attribution and licence

Published by
Qdrant
Publisher
Qdrant (Official)
Source
qdrant/skills · skills/qdrant-edge · commit 9f55f6a
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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