react-native-qdrant-edge

Embedded vector search for React Native. Runs the Qdrant search engine in-process on the device — no server, no network, fully offline.

Built on qdrant-edge 0.7 (Rust) with Nitro Modules for near-zero JS↔native overhead.

Features

• Dense, sparse, and multi-vector HNSW search
• On-device BM25 sparse embedding (no embedding model to ship)
• Hybrid search via prefetch + fusion (RRF, DBSF)
• Advanced query modes: recommend, discover, context, MMR (diversity), order-by, sample
• Faceting — count points per unique value of a payload key
• Snapshot interop with cloud Qdrant
• Structured payload filtering (must / should / must_not / min_should)
• Filter-based payload updates (set / overwrite / delete / clear)
• Dynamic vector slots (add / remove named vectors at runtime)
• Runtime HNSW + optimizer config tuning
• Mobile-tuned WAL options
• UUID and u64 point IDs
• Persistent storage — survives app restarts
• Multiple independent shards
• React hooks API
• iOS and Android (Expo + bare RN)

Install

npm install react-native-qdrant-edge react-native-nitro-modules

Prebuilt native binaries for iOS (arm64 + simulator) and Android (arm64 + x86_64) are included — no Rust toolchain required.

Expo

{ "plugins": ["react-native-qdrant-edge"] }

npx expo run:ios
npx expo run:android

Bare React Native

cd ios && pod install

Quick start

import { createShard, loadShard } from 'react-native-qdrant-edge'

// Create a new shard
const shard = createShard('/path/to/shard', {
  vectors: { '': { size: 384, distance: 'Cosine' } },
})

// Insert points
shard.upsert([
  { id: 1, vector: [0.1, 0.2, /* … */], payload: { title: 'Hello' } },
  { id: 2, vector: [0.3, 0.4, /* … */], payload: { title: 'World' } },
])

// Search
const results = shard.search({
  vector: [0.1, 0.2, /* … */],
  limit: 10,
  with_payload: true,
})

// Persist + reload
shard.flush()
shard.close()

const loaded = loadShard('/path/to/shard')
console.log(loaded.count())  // 2

API

createShard(path, config)

Create a new shard at the given filesystem path. The full config:

const shard = createShard(path, {
  vectors: {
    '':       { size: 384, distance: 'Cosine' },        // default vector
    'title':  { size: 128, distance: 'Dot' },           // additional named dense
    'image':  { size: 512, distance: 'Cosine', on_disk: true },
  },
  sparse_vectors: {
    'bm25': { modifier: 'idf' },                         // BM25 sparse slot
  },
  on_disk_payload: true,
  hnsw_config:    { m: 16, ef_construct: 100 },
  optimizers:     { default_segment_number: 2, indexing_threshold: 20_000 },
  wal_options:    { segment_capacity: 4 * 1024 * 1024 }, // mobile-friendly 4 MiB
})

Distance metrics: Cosine | Euclid | Dot | Manhattan

For a mobile-tuned WAL preset, see mobileWalDefaults().

loadShard(path, config?)

Load an existing shard. Config is optional — if omitted, the stored config is used.

Shard data

// Mixed dense + sparse + multi vectors per point are supported
shard.upsert([
  {
    id: 'a3f1-...-uuid',
    vector: {
      dense: [0.1, 0.2, /* … */],
      bm25:  { indices: [42, 7, 1003], values: [1.0, 1.0, 1.0] },
    },
    payload: { title: 'Mixed', category: 'docs' },
  },
])

shard.deletePoints([1, 2, 'a3f1-...-uuid'])

// Point IDs are u64 numbers OR UUID strings
shard.updateVectors  // (single-point updates use upsert)

// Payload — single point convenience
shard.setPayload(1, { tag: 'new' })
shard.overwritePayload(1, { tag: 'final' })
shard.deletePayload(1, ['old_key'])

// Payload — full power (filter / batch / nested key)
shard.setPayloadOp({
  payload: { archived: true },
  filter:  { must: [{ key: 'created_at', range: { lt: 1_700_000_000 } }] },
})
shard.overwritePayloadOp({ payload, points: [1, 2, 3] })
shard.deletePayloadOp({ keys: ['stale_key'], filter: { /* … */ } })
shard.clearPayload({ filter: { must: [{ key: 'archived', match: { value: true } }] } })

// Field indexes — required for filtering at scale
shard.createFieldIndex('category', 'keyword')
shard.createFieldIndex('price', 'float')
shard.deleteFieldIndex('category')

Field index types: keyword | integer | float | geo | text | bool | datetime

Search

const results = shard.search({
  vector: [0.1, 0.2, /* … */],         // dense | { indices, values } | [[…]] (multi)
  using: 'dense',                       // omit for the default vector
  limit: 10,
  offset: 0,
  with_payload: true,
  with_vector: false,
  score_threshold: 0.5,
  filter: {
    must: [{ key: 'category', match: { value: 'electronics' } }],
  },
})
// [{ id: '1', score: 0.98, payload: { category: '…' } }, …]

Hybrid query (dense + BM25 sparse)

The query API mirrors the upstream qdrant-client REST shape. A prefetch tree fans out one search per vector type, then a fusion clause merges the rankings.

import { createBm25 } from 'react-native-qdrant-edge'

const bm25 = createBm25({ language: 'english' })
const dense = await embedDense('quick brown fox')             // your dense model
const sparse = bm25.embedQuery('quick brown fox')             // on-device BM25

const results = shard.query({
  prefetch: [
    { query: dense,  using: 'dense', limit: 100 },
    { query: sparse, using: 'bm25',  limit: 100 },
  ],
  query:  { fusion: 'rrf' },          // or 'dbsf'
  limit:  10,
  with_payload: true,
})

bm25.close()

fusion: { fusion: 'rrf', k: 60, weights: [2.0, 1.0] } accepts an optional RRF k and per-source weights. Prefetches nest arbitrarily.

Advanced query modes

All of these go in the query slot at the root or within a prefetch:

// Recommend (positive + negative examples)
shard.query({ query: { recommend: { positive: [v1, v2], negative: [v3], strategy: 'best_score' } } })

// Discover (target + positive/negative context pairs)
shard.query({ query: { discover: { target: v, context: [{ positive: p1, negative: n1 }] } } })

// Context (no target, just preference pairs)
shard.query({ query: { context: [{ positive: p1, negative: n1 }] } })

// Order by payload field
shard.query({ query: { order_by: { key: 'created_at', direction: 'desc' } }, limit: 20 })

// Random sample
shard.query({ query: { sample: 'random' }, limit: 50 })

// MMR — diversity-aware rerank (lambda 0 = full diversity, 1 = full relevance)
shard.query({ query: { mmr: { vector: v, lambda: 0.5, candidates_limit: 100 } }, limit: 10 })

Retrieve & scroll

const points = shard.retrieve([1, 2, 'uuid-…'], { withPayload: true, withVector: false })

const { points, next_offset } = shard.scroll({ limit: 100, with_payload: true })

const total = shard.count()
const active = shard.count({ must: [{ key: 'active', match: { value: true } }] })

Facet

Count points per unique value of a payload key.

const { hits } = shard.facet({
  key: 'category',
  limit: 20,
  filter: { must: [{ key: 'in_stock', match: { value: true } }] },
  exact: true,
})
// [{ value: 'electronics', count: 42 }, { value: 'books', count: 17 }, …]

Snapshot interop

Treat the snapshot manifest as opaque — pass it back through recoverPartialSnapshot verbatim.

import { unpackSnapshot, recoverPartialSnapshot } from 'react-native-qdrant-edge'

// Apply an external snapshot to an existing local shard
unpackSnapshot('/downloads/snapshot.tar', '/tmp/snapshot-unpacked')

const current  = shard.snapshotManifest()
const incoming = JSON.parse(await fs.readTextFile('/tmp/snapshot-unpacked/manifest.json'))

const merged = recoverPartialSnapshot(shard.path, current, '/tmp/snapshot-unpacked', incoming)

Lifecycle

shard.flush()       // persist to disk
shard.optimize()    // merge segments, build HNSW indexes
shard.info()        // { points_count, segments_count, indexed_vectors_count }
shard.close()       // flush + release

Runtime config

shard.setHnswConfig({ m: 32, ef_construct: 200 })
shard.setVectorHnswConfig('bm25', { full_scan_threshold: 5000 })
shard.setOptimizersConfig({ indexing_threshold: 10_000, prevent_unoptimized: true })

shard.createVectorName('caption', { dense: { size: 768, distance: 'Cosine' } })
shard.deleteVectorName('legacy')

Mobile WAL preset

The upstream default WAL segment capacity is 32 MiB — wasteful on phones. Use the helper:

import { createShard, mobileWalDefaults } from 'react-native-qdrant-edge'

const shard = createShard(path, {
  vectors: { '': { size: 384, distance: 'Cosine' } },
  wal_options: mobileWalDefaults(),     // 4 MiB segments, retain_closed: 1
})

Filtering

Filters follow the Qdrant filter syntax:

{
  must: [
    { key: 'price', range: { gte: 10, lte: 100 } },
    { key: 'category', match: { value: 'shoes' } },
  ],
  should: [
    { key: 'brand', match: { any: ['Nike', 'Adidas'] } },
  ],
  must_not: [
    { key: 'archived', match: { value: true } },
  ],
}

Error handling

Every Shard / Bm25 method that fails throws a JS Error with a message of the form "<operation> failed: <cause>". For structured access:

import { asQdrantError } from 'react-native-qdrant-edge'

try {
  shard.upsert(points)
} catch (err) {
  const qe = asQdrantError(err)
  console.log(qe.operation, qe.cause)   // e.g. 'upsert', 'invalid JSON path: …'
}

React hooks

import {
  useShard, useUpsert, useDelete,
  useSearch, useQuery,
  useRetrieve, useScroll, useCount, useShardInfo,
  useBm25, useFacet, useSnapshotManifest,
} from 'react-native-qdrant-edge'

function NotesScreen() {
  const { shard, open, close } = useShard({
    path: `${documentDir}/notes`,
    config: { vectors: { '': { size: 384, distance: 'Cosine' } } },
    create: true,
  })

  const { bm25 } = useBm25({ language: 'english' })

  const { results, search } = useSearch({
    shard,
    request: { vector: queryEmbedding, limit: 10, with_payload: true },
    enabled: true,
  })

  useEffect(() => { open() }, [])
  // shard + bm25 are auto-closed/disposed on unmount

  return <NotesList shard={shard} bm25={bm25} results={results} />
}

Hybrid search via useQuery:

const { results } = useQuery({
  shard,
  request: {
    prefetch: [
      { query: denseVec,  using: 'dense', limit: 100 },
      { query: sparseVec, using: 'bm25',  limit: 100 },
    ],
    query: { fusion: 'rrf' },
    limit: 10,
    with_payload: true,
  },
})

Multiple shards

Each shard is independent — separate storage, config, and indexes.

const docs = createShard(`${dir}/docs`,   { vectors: { '': { size: 768, distance: 'Cosine' } } })
const imgs = createShard(`${dir}/photos`, { vectors: { '': { size: 512, distance: 'Dot' } } })

Migration from 0.2.x

Mostly additive. The only TS-visible widening is:

• Point.id and IDs in deletePoints / retrieve: number → number | string. Existing numeric IDs still work.
• Point.vector and SearchRequest.vector: accept sparse { indices, values } and multi [[…]] in addition to dense.
• Shard.setPayload(pointId, payload) gains an optional 3rd argument key?: string and accepts string IDs. Existing call sites are unchanged.

Note one upstream behavior change: with optimizers.prevent_unoptimized: true, points written to unoptimized segments above indexing_threshold are persisted as deferred — they are excluded from reads/search until you call shard.optimize(). Previously this option blocked the write entirely.

Architecture

TypeScript API
  → Nitro HybridObject (C++, near-zero JS overhead)
    → extern "C" FFI
      → qdrant-edge 0.7 (Rust)
        → HNSW index, WAL, segment storage, BM25 tokenizer

All operations are synchronous and run on the JS thread via JSI — no bridge, no serialization between JS and the C++ object. Vector and metadata payloads cross the FFI boundary as JSON; the JSON-parse overhead is negligible vs HNSW lookup for search, and measurable but acceptable for bulk upsert (raw Float32Array marshaling is on the roadmap — see Future work).

Building from source

Only needed if you contribute, or the prebuilt binaries don't cover your target.

Requirements

• Rust
• Xcode (iOS)
• Android NDK (Android)
• cbindgen for header regen: cargo install cbindgen

Build

npm run rust:build:ios          # xcframework: device arm64 + simulator
npm run rust:build:android      # arm64 + x86_64
npm run rust:build              # both

After modifying any .nitro.ts, regenerate the bindings:

npm run specs

Future work

• ArrayBuffer / Float32Array for vectors — skip JSON encoding for bulk upsert (HNSW search itself is already JSON-light).
• Async / background-thread operations — offload optimize, bulk upsert, and snapshotManifest via Nitro async methods.
• Formula rescoring — the upstream AST does not impl Deserialize; will need a typed expression builder API.
• gRPC client wrapper — out of scope, but could ship alongside.

License

MIT