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@wiscale/velesdb-sdk

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@wiscale/velesdb-sdk

Official TypeScript SDK for VelesDB -- the local-first vector database for AI and RAG. Sub-millisecond semantic search in Browser and Node.js.

v3.4.0 | Node.js >= 18 | Browser (WASM) | VelesDB Core License 1.0

What's New (unreleased)

  • Streaming ingestion enablement (2026-06-14) (REST backend): enableStreaming(collection, config?) turns on the bounded streaming-ingestion channel before streamInsert(). The optional StreamingConfig (bufferSize, batchSize, flushIntervalMs) is camelCase; omitted fields fall back to the server defaults. See db.enableStreaming below. The WASM backend throws NOT_SUPPORTED.
  • Relation + durable-TTL surface (REST backend): relate(), unrelate(), getRelations(), setTtlDurable() — now fully tested and documented (see Knowledge Graph API and Agent Memory API below). The WASM backend throws NOT_SUPPORTED for these methods.
  • The shipped example (examples/hybrid_queries.ts) was rewritten against the real API and is now compile-checked in CI.

What's New in v2.0.0

  • v2.0.0: graph dimension on agent memory — relate() / relations() / unrelate(); durable-TTL setters; aligns with the engine 2.0 release. See the root CHANGELOG for the breaking VelesQL changes.
  • v1.18.0: agent-memory parity wave — temporal recall facades (recallRecent / recallOlderThan), id-coercion hardening for deleteMemory(string | number).

What's New in v1.16.0

  • First-party embedding helper. New OpenAIEmbedder (plus the Embedder interface and OpenAIEmbedderOptions type), exported from the package root. It calls any OpenAI-compatible /embeddings endpoint via the global fetch API — no extra runtime dependency — so you can go from text to vectors without hand-writing the request. See Embedding helper below. Works in Node.js ≥ 18, browsers, and Deno.
Previous (v1.14.2)
  • No SDK source change. v1.14.2 was a workspace patch focused on the Python Haystack DocumentStore (DuplicatePolicy.SKIP contract fix) and seven version-drift gaps in the release tooling. The TS SDK ships in lock-step with the workspace and was functionally identical to v1.14.1.
Previous (v1.14.1)
  • Pipeline fix only. v1.14.0 added Haystack 2.x DocumentStore source code on the Python side but the release workflow forgot to publish haystack-velesdb to PyPI. v1.14.1 closes that gap. No TS SDK source change.
Previous (v1.14.0)
  • MSRV Rust 1.89 -- workspace and CI now align with the actual SIMD path (avx512vpopcntdq target feature). No source change for the SDK; bumps in lock-step with the workspace.
  • Dockerfile auto-sync -- release tooling now keeps LABEL version= in lock-step across all Dockerfiles. Indirectly improves anyone running docker build against a checkout.
Previous (v1.13.7)
  • Node.js WASM init fix -- new VelesDB({ backend: 'wasm' }).init() now reads velesdb_wasm_bg.wasm bytes from disk via fs.readFile so Node 20+ no longer crashes on the broken fetch('file://') path. Browsers are unchanged.
  • Lifecycle hardening -- memoised in-flight init promise + generation token make close() race-free.
  • Dual ESM + CJS bundles -- TS SDK build emits both formats with import.meta.url/__filename polyfilled correctly.
Previous (v1.13.0)
  • WASM VelesQL executor -- full browser-side VelesQL execution: SELECT/INSERT/UPDATE/DELETE/DDL + aggregations (COUNT/SUM/AVG/MIN/MAX) + GROUP BY/HAVING/UNION/INTERSECT/EXCEPT/JOIN/FUSION/MATCH 1-2 hops + NOT De Morgan distribution
  • TS SDK coverage raised -- per-file thresholds codified in vitest.config.ts (423 tests as of v1.13.0; the vitest suite has since grown past 770 cases). Note: the suite runs locally via npm test and is not currently executed in CI
  • SIFT1M standardized ANN benchmark -- fvecs/ivecs loader + Criterion ef sweep on the INRIA TEXMEX dataset, feature-gated behind --features bench-sift1m
  • Security hardening -- validateCollectionName() helper on TS SDK prevents VelesQL injection in trainPq
  • API consistency -- streamInsert now serializes payload: null explicitly (matches streamUpsertPoints)
Previous (v1.12.0)
  • Cross-collection MATCH queries -- @collection annotation on MATCH node patterns enables cross-collection graph queries
  • MATCH via /query endpoint -- MATCH queries can now be executed via Database::execute_query
  • BFS dedup -- CSR and EdgeStore BFS no longer produce duplicate results for diamond graphs
  • rrf_k propagation -- now properly propagated to hybrid_search_with_filter
  • ComponentScores optimization -- changed to &'static str for zero-allocation score tagging
Previous (v1.11.1)
  • Graph API parity -- 7 new REST endpoints for complete graph operations (delete edge, edge count, list nodes, node edges, node payload, parallel BFS, graph search)
  • Bitmap pre-filter -- adaptive strategy selection for filtered search
  • CSR graph traversal v2 -- lock-free adjacency with edge IDs and labels
  • Bulk insert v2 -- DirectVectorWriter + AsyncIndexBuilder pipeline
Previous (v1.11.0)
  • 15 new VelesQL statements -- SHOW COLLECTIONS, DESCRIBE, EXPLAIN, CREATE/DROP INDEX, ANALYZE, TRUNCATE, ALTER COLLECTION, FLUSH, multi-row INSERT, UPSERT, SELECT EDGES, INSERT NODE
  • 203 BDD E2E tests -- comprehensive end-to-end test coverage for all VelesQL features
  • TRUNCATE on graph collections -- clears nodes + edges in a single statement
  • Python execute_query() -- full VelesQL execution from Python bindings
  • Cyclomatic complexity ≤ 8 -- refactored 6 hotspots for Codacy compliance
Previous (v1.10.0)
  • SearchQuality type -- SearchQuality type and quality field in SearchOptions
  • StorageMode in HnswParams -- storageMode field in HNSW configuration
  • Relative score fusion -- 'relative_score' fusion strategy
  • DistanceMetric "ip" alias -- "ip" accepted as alias for "dot"
  • StorageMode aliases -- "f32", "int8", "bit" accepted
Previous (v1.9.1)
  • Agent Memory API -- semantic, episodic, and procedural memory for AI agents (REST only)
  • Graph collections -- dedicated createGraphCollection() for knowledge graphs (REST only)
  • Metadata-only collections -- reference tables with no vectors, joinable via VelesQL
  • Sparse vector support -- hybrid sparse+dense search on insert and query (REST + WASM)
  • Stream insert with backpressure -- streamInsert() for high-throughput ingestion (REST only)
  • Product Quantization training -- trainPq() for further memory compression (REST only)
  • Collection analytics -- analyzeCollection(), getCollectionStats(), getCollectionConfig() (REST only)
  • Property indexes -- createIndex() / listIndexes() / dropIndex() for O(1) lookups (REST only)
  • Query introspection -- queryExplain() and collectionSanity() diagnostics (REST only)
  • Batch search -- searchBatch() for parallel multi-query execution
  • Lightweight search -- searchIds() returns only IDs and scores (REST only)

Installation

npm install @wiscale/velesdb-sdk
Supported Node.js versions
Node.js TS SDK Browser (WASM) Notes
18 LTS minimum supported
20 LTS (CI) tested matrix on every PR
22 LTS latest LTS

The Node.js WASM init() path was fixed in v1.13.7 to read velesdb_wasm_bg.wasm bytes from disk via fs.readFile instead of the broken fetch('file://') URL — Node ≥ 18 now works out-of-the-box. Browsers were never affected.

Quick Start

WASM Backend (Browser / Node.js)

The WASM backend runs entirely in-process -- no server required. Ideal for browser apps, prototyping, and edge deployments.

import { VelesDB } from '@wiscale/velesdb-sdk';

// 1. Create a client with WASM backend
const db = new VelesDB({ backend: 'wasm' });
await db.init();

// 2. Create a collection (768 dimensions for BERT, 1536 for OpenAI, etc.)
await db.createCollection('documents', {
  dimension: 768,
  metric: 'cosine'
});

// 3. Upsert vectors with metadata
await db.upsert('documents', {
  id: 'doc-1',
  vector: new Float32Array(768).fill(0.1),
  payload: { title: 'Hello World', category: 'greeting' }
});

// 4. Batch upsert for better throughput
await db.upsertBatch('documents', [
  { id: 'doc-2', vector: new Float32Array(768).fill(0.2), payload: { title: 'Second doc' } },
  { id: 'doc-3', vector: new Float32Array(768).fill(0.3), payload: { title: 'Third doc' } },
]);

// 5. Search for similar vectors
const queryVector = new Float32Array(768).fill(0.1);
const results = await db.search('documents', queryVector, { k: 5 });
console.log(results);
// [{ id: 'doc-1', score: 0.95, payload: { title: 'Hello World', ... } }, ...]

// 6. Cleanup
await db.close();
REST Backend (Server)

The REST backend connects to a running VelesDB server. Use this for production deployments, multi-client access, and persistent storage.

import { VelesDB } from '@wiscale/velesdb-sdk';

const db = new VelesDB({
  backend: 'rest',
  url: 'http://localhost:8080',
  apiKey: 'your-api-key'  // optional
});

await db.init();

// Same API as WASM backend
await db.createCollection('products', { dimension: 1536 });
await db.upsert('products', { id: 1, vector: embedding });
const queryVector = new Float32Array(1536).fill(0.1);
const results = await db.search('products', queryVector, { k: 10 });

REST backend note: Document IDs must be non-negative u64 integers. Pass a JS number in the range 0..Number.MAX_SAFE_INTEGER, or a decimal string for the full u64 range — string ids above 2^53-1 (up to 18446744073709551615) are kept verbatim on the wire, so the ids returned by recordEvent/learnProcedure round-trip through get/delete without precision loss. Exception: the NDJSON bulk endpoint (streamUpsertPoints) only accepts safe-range numeric ids. Arbitrary (non-numeric) string IDs are only supported with the WASM backend.

Versioned routes: The REST backend uses /v1/ as the canonical route prefix (e.g. POST /v1/collections/{name}/search). Legacy routes without the prefix are accepted for backward compatibility but are deprecated and will be removed in a future major version. Always target /v1/ in custom HTTP clients.

Embedding helper

The SDK ships an optional OpenAIEmbedder so you can turn text into vectors without writing the HTTP call yourself. It targets any OpenAI-compatible /embeddings endpoint (OpenAI, Azure OpenAI, vLLM, …) using the global fetch API, so it adds no extra runtime dependency and runs in Node.js ≥ 18, browsers, and Deno.

import { VelesDB, OpenAIEmbedder } from '@wiscale/velesdb-sdk';

const embedder = new OpenAIEmbedder({
  apiKey: process.env.OPENAI_API_KEY!,
  model: 'text-embedding-3-small', // default; pass `dimensions` to truncate
});

const db = new VelesDB({ backend: 'wasm' });
await db.init();

// Embed first so the collection dimension matches the model output.
const vectors = await embedder.embed(['hello world', 'vector search']);
await db.createCollection('docs', { dimension: embedder.dimension, metric: 'cosine' });
await db.upsertBatch('docs', vectors.map((vector, i) => ({ id: `doc-${i}`, vector })));

const [query] = await embedder.embed(['greeting']);
const results = await db.search('docs', query, { k: 5 });

embedder.dimension is 0 until the first embed() call (or until you pass dimensions to the constructor), at which point it is inferred from the model's output. To use a different provider, set baseUrl. Implement the Embedder interface ({ dimension: number; embed(texts: string[]): Promise<number[][]> }) to plug in any other embedding source.

API Reference

Client
new VelesDB(config)

Create a new VelesDB client.

Option Type Required Description
backend 'wasm' | 'rest' Yes Backend type
url string REST only Server URL
apiKey string No API key for authentication
timeout number No Request timeout in ms (default: 30000)
db.init()

Initialize the client. Must be called before any operations. For the REST backend, this verifies connectivity to the server.

db.close()

Close the client and release resources.

Collection Management
db.createCollection(name, config)

Create a vector collection.

Option Type Default Description
dimension number Required Vector dimension
metric 'cosine' | 'euclidean' | 'dot' | 'hamming' | 'jaccard' 'cosine' Distance metric (aliases: 'ip'/'inner'/'dotproduct' for dot)
storageMode 'full' | 'sq8' | 'binary' 'full' Quantization mode (aliases: 'f32' for full, 'int8' for sq8, 'bit' for binary)
hnsw { m?: number, efConstruction?: number } - HNSW index tuning
description string - Optional description
Storage Modes
Mode Memory (768D) Compression Use Case
full 3 KB/vector 1x Default, max precision
sq8 776 B/vector 4x Production scale, RAM-constrained
binary 96 B/vector 32x Edge devices, IoT 
await db.createCollection('embeddings', {
  dimension: 768,
  metric: 'cosine',
  storageMode: 'sq8',
  hnsw: { m: 16, efConstruction: 200 }
});
db.createGraphCollection(name, config?)

Create a dedicated graph collection for knowledge graph workloads.

await db.createGraphCollection('social', {
  dimension: 384,       // optional: embed nodes for vector+graph queries
  metric: 'cosine',
  schemaMode: 'schemaless'  // or 'strict'
});
db.createMetadataCollection(name)

Create a metadata-only collection (no vectors). Useful for reference tables that can be JOINed with vector collections via VelesQL.

await db.createMetadataCollection('products');
db.deleteCollection(name)

Delete a collection and all its data.

db.getCollection(name)

Get collection info. Returns null if not found.

db.listCollections()

List all collections. Returns an array of Collection objects.

Upsert and Retrieve
db.upsert(collection, document)

Upsert (insert or replace) a single vector document.

await db.upsert('docs', {
  id: 'unique-id',
  vector: [0.1, 0.2, 0.3],    // number[] or Float32Array
  payload: { key: 'value' },   // optional metadata
  sparseVector: { 42: 0.8, 99: 0.3 }  // optional sparse vector for hybrid search
});
db.upsertBatch(collection, documents)

Upsert multiple vectors in a single call. More efficient than repeated upsert().

await db.upsertBatch('docs', [
  { id: 'a', vector: vecA, payload: { title: 'First' } },
  { id: 'b', vector: vecB, payload: { title: 'Second' } },
]);
db.enableStreaming(collection, config?)

Enable the bounded streaming-ingestion channel on a collection. Call this once before streamInsert(). The optional config is camelCase; every field is optional and omitted fields fall back to the server defaults (REST only; the WASM backend throws NOT_SUPPORTED).

Option Type Default Description
bufferSize number 10000 Bounded ingestion channel capacity
batchSize number 128 Points flushed to the index per batch
flushIntervalMs number 50 Max milliseconds before a partial batch is flushed 
await db.enableStreaming('docs', { bufferSize: 4096, batchSize: 64 });
await db.streamInsert('docs', largeDocumentArray);
db.streamInsert(collection, documents)

Insert documents with server backpressure support. Sends documents sequentially, respecting 429 rate limits. Throws BackpressureError if the server pushes back.

await db.streamInsert('docs', largeDocumentArray);
db.get(collection, id)

Get a document by ID. Returns null if not found.

db.delete(collection, id)

Delete a document by ID. Returns true if deleted, false if not found.

Search
db.search(collection, query, options?)

Vector similarity search.

Option Type Default Description
k number 10 Number of results
filter object - Payload filter expression
includeVectors boolean false Include vectors in results
sparseVector Record<number, number> - Sparse vector for hybrid sparse+dense search
quality SearchQuality - Search quality mode (e.g., 'fast', 'balanced', 'custom:256', 'adaptive:32:512') 
const results = await db.search('docs', queryVector, {
  k: 10,
  filter: { condition: { type: 'eq', field: 'category', value: 'tech' } },
  includeVectors: true
});
// Returns: SearchResult[] = [{ id, score, payload?, vector? }, ...]
db.searchBatch(collection, searches)

Execute multiple search queries in parallel.

const batchResults = await db.searchBatch('docs', [
  { vector: queryA, k: 5 },
  { vector: queryB, k: 10, filter: { condition: { type: 'eq', field: 'type', value: 'article' } } },
]);
// Returns: SearchResult[][] (one result array per query)
db.searchIds(collection, query, options?)

Lightweight search returning only IDs and scores (no payloads).

const hits = await db.searchIds('docs', queryVector, { k: 100 });
// Returns: Array<{ id: number, score: number }>
db.scroll(collection, request)

Iterate over all points in a collection in stable, paginated batches without a query vector. Useful for export pipelines, re-embedding, and full-collection inspection. Pagination is cursor-based — pass the nextCursor from each ScrollResponse until it is null.

import { ScrollRequest, ScrollResponse } from '@wiscale/velesdb-sdk';

let cursor: string | number | null = null;
let hasMore = true;
while (hasMore) {
  const req: ScrollRequest = { batchSize: 100 };
  if (cursor !== null) {
    req.cursor = cursor;
  }

  const page: ScrollResponse = await db.scroll('docs', req);

  for (const point of page.points) {
    console.log(point.id, point.payload);
  }
  cursor = page.nextCursor;
  hasMore = cursor !== null;
}
Field (ScrollRequest) Type Default Description
cursor string | number - Cursor from previous ScrollResponse.nextCursor
batchSize number 100 Points per page
filter object - Optional payload filter expression
db.textSearch(collection, query, options?)

Full-text search using BM25 scoring.

const results = await db.textSearch('docs', 'machine learning', { k: 10 });
db.hybridSearch(collection, vector, textQuery, options?)

Combined vector similarity + BM25 text search with RRF fusion.

const results = await db.hybridSearch(
  'docs',
  queryVector,
  'machine learning',
  { k: 10, vectorWeight: 0.7 }  // 70% vector, 30% text
);
db.multiQuerySearch(collection, vectors, options?)

Multi-query fusion search for RAG pipelines using Multiple Query Generation (MQG). Combines results from several query vectors into a single ranked list.

Option Type Default Description
k number 10 Number of results
fusion 'rrf' | 'average' | 'maximum' | 'weighted' | 'relative_score' 'rrf' Fusion strategy
fusionParams object { k: 60 } Strategy-specific parameters
filter object - Payload filter expression 
// RRF fusion (default) -- best for most RAG use cases
const results = await db.multiQuerySearch('docs', [emb1, emb2, emb3], {
  k: 10,
  fusion: 'rrf',
  fusionParams: { k: 60 }
});

// Weighted fusion
const results = await db.multiQuerySearch('docs', [emb1, emb2], {
  k: 10,
  fusion: 'weighted',
  fusionParams: { avgWeight: 0.6, maxWeight: 0.3, hitWeight: 0.1 }
});

// Relative Score Fusion — linear blend of dense and sparse scores
const results = await db.multiQuerySearch('docs', [emb1, emb2], {
  k: 10,
  fusion: 'relative_score',
  fusionParams: { denseWeight: 0.7, sparseWeight: 0.3 }
});

Note: WASM supports rrf, average, maximum. The weighted and relative_score strategies are REST-only.

Named sparse indexes — sparseIndexName vs sparseSearchNamed()

A collection can carry multiple sparse indexes (e.g. splade_v2, bm25_titles). The TypeScript SDK exposes two distinct APIs depending on whether you want a dense + sparse hybrid query or a pure sparse query against a named index.

API Shape Use when
db.search(coll, denseVec, { sparseVector, sparseIndexName }) dense + sparse hybrid against the named sparse index You already have a dense embedding and want to combine it with a specific named sparse index in a single search call. The dense vector drives the primary candidate set; the named sparse index re-ranks.
db.sparseSearchNamed(coll, sparseVec, indexName, options?) pure sparse against a named index You have only a sparse vector (e.g. SPLADE expansion, lexical query) and want to query a specific named sparse index directly. No dense component. 
// Hybrid: dense + sparse via the splade_v2 named index
const hybrid = await db.search('docs', denseEmbedding, {
  k: 10,
  sparseVector: { 42: 0.8, 99: 0.3 },
  sparseIndexName: 'splade_v2',
});

// Pure sparse against the same named index
const sparseOnly = await db.sparseSearchNamed(
  'docs',
  { 42: 0.8, 99: 0.3 },
  'splade_v2',
  { k: 10 },
);

When the collection has only one (default) sparse index, omit sparseIndexName on db.search(); the server picks the default. For named indexes, both APIs require the explicit name.

Note: Both APIs are REST-only when a named index is required. The WASM backend has no concept of named sparse indexes — sparseIndexName is silently ignored on db.search() (the collection's single sparse index is used), and db.sparseSearchNamed() throws wasmNotSupported. Tracked as a follow-up to either surface a wasmNotSupported throw on sparseIndexName or implement named-sparse support in WASM.

Collection Utilities
db.isEmpty(collection)

Returns true if the collection contains no vectors.

db.flush(collection)

Flush pending changes to disk. REST backend only -- the WASM backend runs in-memory and this is a no-op.

db.analyzeCollection(collection)

Compute and return collection statistics.

const stats = await db.analyzeCollection('docs');
console.log(stats.totalPoints, stats.totalSizeBytes);
db.getCollectionStats(collection)

Get previously computed statistics. Returns null if the collection has not been analyzed yet.

db.getCollectionConfig(collection)

Get detailed collection configuration (dimension, metric, storage mode, point count, schema).

VelesQL Queries
db.query(collection, queryString, params?, options?)

Execute a VelesQL query. Supports SELECT, WHERE, vector NEAR, GROUP BY, HAVING, ORDER BY, JOIN, UNION/INTERSECT/EXCEPT, and USING FUSION.

Backend support: full VelesQL execution requires the REST backend (velesdb-server). The WASM backend only executes pure top-k vector queries of the form SELECT * FROM <collection> WHERE vector NEAR $param [LIMIT n] (vector is the grammar keyword, not a column name) and throws a NOT_SUPPORTED error for anything else (WHERE predicates, JOIN, GROUP BY, MATCH, set operations, FUSION) instead of silently ignoring clauses. Accordingly, db.capabilities().velesqlQuery is false on WASM.

// Vector similarity search
const result = await db.query(
  'documents',
  'SELECT * FROM documents WHERE VECTOR NEAR $query LIMIT 5',
  { query: [0.1, 0.2, 0.3] }
);

// Aggregation
const agg = await db.query(
  'products',
  `SELECT category, COUNT(*), AVG(price)
   FROM products
   GROUP BY category
   HAVING COUNT(*) > 5`
);

// Hybrid vector + text
const hybrid = await db.query(
  'docs',
  "SELECT * FROM docs WHERE VECTOR NEAR $v AND content MATCH 'rust' LIMIT 10",
  { v: queryVector }
);

// Cross-collection JOIN
const joined = await db.query(
  'orders',
  `SELECT * FROM orders
   JOIN customers AS c ON orders.customer_id = c.id
   WHERE status = $status`,
  { status: 'active' }
);

// Set operations
const combined = await db.query('users',
  'SELECT * FROM active_users UNION SELECT * FROM archived_users'
);

// Fusion strategy
const fused = await db.query('docs',
  "SELECT * FROM docs LIMIT 20 USING FUSION(strategy = 'rrf', k = 60)"
);

Query options:

Option Type Default Description
timeoutMs number 30000 Query timeout in milliseconds
stream boolean false Enable streaming response
db.queryExplain(queryString, params?)

Get the execution plan for a VelesQL query without running it. Returns plan steps, estimated cost, index usage, and detected features.

const plan = await db.queryExplain(
  'SELECT * FROM docs WHERE VECTOR NEAR $v LIMIT 10',
  { v: queryVector }
);
console.log(plan.plan);           // step-by-step execution plan
console.log(plan.estimatedCost);  // { usesIndex, selectivity, complexity }
console.log(plan.features);       // { hasVectorSearch, hasFilter, hasJoin, ... }
db.collectionSanity(collection)

Run diagnostic checks on a collection (dimensions, search readiness, error counts, hints).

const report = await db.collectionSanity('docs');
console.log(report.checks);       // { hasVectors, searchReady, dimensionConfigured }
console.log(report.diagnostics);   // { searchRequestsTotal, dimensionMismatchTotal, ... }
console.log(report.hints);         // actionable suggestions
VelesQL Query Builder

Build type-safe VelesQL queries with a fluent API instead of raw strings.

import { velesql } from '@wiscale/velesdb-sdk';

// Vector similarity with filters — use SELECT mode via from()
const builder = velesql()
  .from('documents', 'd')
  .nearVector('$queryVector', queryVector)
  .andWhere('d.category = $cat', { cat: 'tech' })
  .orderBy('similarity()', 'DESC')
  .limit(10);

const queryString = builder.toVelesQL();
// => "SELECT * FROM documents WHERE vector NEAR $queryVector AND d.category = $cat ORDER BY similarity() DESC LIMIT 10"
const params = builder.getParams();
const results = await db.query('documents', queryString, params);

// Graph traversal with relationships (MATCH mode — RETURN is mandatory
// and precedes ORDER BY / LIMIT)
const graphQuery = velesql()
  .match('p', 'Person')
  .rel('KNOWS')
  .to('f', 'Person')
  .where('p.age > 25')
  .return(['p.name', 'f.name'])
  .toVelesQL();
// => "MATCH (p:Person)-[:KNOWS]->(f:Person) WHERE p.age > 25 RETURN p.name, f.name"

// Hybrid (vector NEAR + text MATCH) with a real USING FUSION clause
const hybrid = velesql()
  .from('docs')
  .nearVector('$q', queryVector)
  .andWhere("content MATCH 'invoice'")
  .limit(10)
  .fusion('weighted', { vectorWeight: 0.7, graphWeight: 0.3 })
  .toVelesQL();
// => "SELECT * FROM docs WHERE vector NEAR $q AND content MATCH 'invoice' LIMIT 10 USING FUSION(strategy='weighted', vector_weight=0.7, graph_weight=0.3)"

// Multi-vector fused search — strategy is typed to rrf | average | maximum
const fused = velesql()
  .from('docs')
  .nearFused(['$a', '$b'], [vectorA, vectorB], { strategy: 'average' })
  .limit(10)
  .toVelesQL();
// => "SELECT * FROM docs WHERE vector NEAR_FUSED [$a, $b] USING FUSION 'average' LIMIT 10"

Mode matters. Call from(collection) for vector / hybrid / fused SELECT queries; call match(alias, label) for graph patterns. MATCH mode always emits a RETURN (defaulting to RETURN *) and supports no OFFSET. nearVector({ topK }) maps to LIMIT (VelesQL has no TOP keyword). nearFused() only accepts rrf / average / maximum — weighted / relative_score are a compile error because the engine would silently degrade them to RRF.

Builder methods: match(), from(), select(), rel(), to(), where(), andWhere(), orWhere(), nearVector(), nearFused(), limit(), offset(), orderBy(), return(), returnAll(), fusion().

Collection settings — db.setAutoReindex() / db.alterCollection()

Toggle a collection's mutable settings at runtime via typed helpers that emit ALTER COLLECTION ... SET(...):

await db.setAutoReindex('docs', true);             // auto_reindex=true
await db.alterCollection('docs', { autoReindex: false });
Knowledge Graph API
db.addEdge(collection, edge)

Add a directed edge between two nodes.

await db.addEdge('social', {
  id: 1,
  source: 100,
  target: 200,
  label: 'FOLLOWS',
  properties: { since: '2024-01-01' }
});
db.getEdges(collection, options?)

Query edges, optionally filtered by label.

const edges = await db.getEdges('social', { label: 'FOLLOWS' });
db.traverseGraph(collection, request)

Traverse the graph using BFS or DFS from a source node.

const result = await db.traverseGraph('social', {
  source: 100,
  strategy: 'bfs',
  maxDepth: 3,
  limit: 100,
  relTypes: ['FOLLOWS', 'KNOWS']
});

for (const node of result.results) {
  console.log(`Node ${node.targetId} at depth ${node.depth}`);
}
db.getNodeDegree(collection, nodeId)

Get the in-degree and out-degree of a node.

const degree = await db.getNodeDegree('social', 100);
console.log(`In: ${degree.inDegree}, Out: ${degree.outDegree}`);
db.traverseParallel(collection, request)

Multi-source parallel BFS traversal with deduplication. Starts BFS from multiple source nodes simultaneously.

const result = await db.traverseParallel('social', {
  sources: [100, 200, 300],
  maxDepth: 3,
  limit: 50,
  relTypes: ['FOLLOWS']
});

for (const node of result.results) {
  console.log(`Node ${node.targetId} at depth ${node.depth}`);
}
db.relate(collection, request)

Create a typed relation edge between two existing points. Returns the allocated edge ID (RelateResponse = { edgeId: number | string }). Point and edge IDs are number | string — IDs above Number.MAX_SAFE_INTEGER travel as decimal strings to avoid u64 precision loss. REST backend only (the WASM backend throws NOT_SUPPORTED).

const { edgeId } = await db.relate('social', {
  source: 100,
  target: 200,
  relType: 'FOLLOWS',
  properties: { since: '2024-01-01' }  // optional, defaults to {}
});
db.unrelate(collection, edgeId)

Remove a relation edge by its ID. Returns true if the edge was removed, false if it did not exist. REST backend only.

const removed = await db.unrelate('social', edgeId);
db.getRelations(collection, pointId)

List the outgoing relation edges of a point. Returns RelationsResponse = { edges: RelationEdge[]; count: number } where each edge carries { id, source, target, relType, properties? }. REST backend only.

const { edges, count } = await db.getRelations('social', 100);
for (const edge of edges) {
  console.log(`${edge.source} -[${edge.relType}]-> ${edge.target}`);
}
db.setTtlDurable(collection, pointId, ttlSeconds)

Durably set (or refresh) the time-to-live of a point, in seconds. The expiry is persisted server-side (reserved _veles_expires_at payload field), so it survives a restart. ttlSeconds must be a non-negative number. REST backend only.

await db.setTtlDurable('social', 100, 3600); // expire point 100 in 1 hour
Property Indexes

Create secondary indexes for fast lookups on payload fields.

db.createIndex(collection, options)
// Hash index for O(1) equality lookups
await db.createIndex('users', { label: 'Person', property: 'email' });

// Range index for O(log n) range queries
await db.createIndex('events', {
  label: 'Event',
  property: 'timestamp',
  indexType: 'range'
});
db.listIndexes(collection)
const indexes = await db.listIndexes('users');
// [{ label, property, indexType, cardinality, memoryBytes }, ...]
db.hasIndex(collection, label, property)

Returns true if the specified index exists.

db.dropIndex(collection, label, property)

Drop an index. Returns true if the index existed and was removed.

Product Quantization
db.trainPq(collection, options?)

Train Product Quantization on a collection for further memory compression beyond SQ8. REST backend only -- delegates to velesdb-server; not available in the WASM backend.

const result = await db.trainPq('embeddings', {
  m: 8,       // number of subquantizers
  k: 256,     // centroids per subquantizer
  opq: true   // enable Optimized PQ
});
Agent Memory API

The Agent Memory API provides three memory types for AI agents, built on top of VelesDB's vector storage. In the TypeScript/JavaScript SDK it is accessed over REST against a running velesdb-server (the Python and Rust bindings use the embedded engine instead).

You must create the collection first. The TS facade does not auto-create a collection for you. Create it with the dimension that matches your embedding model and the metric you want ('cosine' is the usual choice for normalized text embeddings), then call storeFact / recordEvent / learnProcedure against that same collection name.

Embeddings are caller-supplied. There is no auto-embedding: every embedding you pass must come from your own embedding model (see Embedding helper) and its length must equal the collection dimension.

import { VelesDB } from '@wiscale/velesdb-sdk';

const db = new VelesDB({ backend: 'rest', url: 'http://localhost:8080' });
await db.init();

// 1. Create the backing collection FIRST (nothing auto-creates it).
//    The dimension must match your embedding model; cosine is typical.
await db.createCollection('knowledge', { dimension: 384, metric: 'cosine' });

// 2. Open the agent-memory facade.
const memory = db.agentMemory({ dimension: 384 });

// 3. Store and recall (embedding is your own model's output, length 384).
await memory.storeFact('knowledge', {
  id: 1,
  text: 'VelesDB uses HNSW for vector indexing',
  embedding: factEmbedding,
});
const facts = await memory.searchFacts('knowledge', queryEmbedding, 5);

Each similarity-recall method (searchFacts, recallEvents, recallProcedures) returns SearchResult[]:

// SearchResult = { id: number; score: number; payload?: Record<string, unknown>; vector?: number[] }

The temporal-recall methods (recallRecent, recallOlderThan) return EpisodicRecord[] instead — see Episodic Memory.

  • score is the cosine similarity in [0, 1] (for a cosine collection); higher means more similar.
  • payload carries the stored fields (content for semantic text, event_type / timestamp for episodic, name / steps for procedural, plus your metadata). The reserved payload keys _memory_type / content / event_type / timestamp / name / steps always take precedence over caller metadata/data of the same name, so they cannot be clobbered. Note: the SemanticEntry.text input field is stored as content in the payload — result.payload?.content is the fact text on recall.
Semantic Memory (facts and knowledge)
// Store a fact (id is caller-assigned; reusing an id upserts)
await memory.storeFact('knowledge', {
  id: 1,
  text: 'VelesDB uses HNSW for vector indexing',
  embedding: factEmbedding,
  metadata: { source: 'docs', confidence: 0.95 }
});

// Recall similar facts
const facts = await memory.searchFacts('knowledge', queryEmbedding, 5);
Episodic Memory (events and experiences)
// Record an event — returns the generated point id
const eventId = await memory.recordEvent('events', {
  eventType: 'user_query',
  data: { query: 'How does HNSW work?', response: '...' },
  embedding: eventEmbedding,
});

// Recall similar events
const events = await memory.recallEvents('events', queryEmbedding, 5);

// Temporal recall — no embedding needed, most-recent-first.
// recallRecent(collection, since?): events with timestamp >= since
// (inclusive, unix-seconds); recallOlderThan(collection, before): strictly older.
const nowSecs = Math.floor(Date.now() / 1000);
const allRecent = await memory.recallRecent('events');
const lastHour = await memory.recallRecent('events', nowSecs - 3600);
const stale = await memory.recallOlderThan('events', nowSecs - 86_400);

// Both return EpisodicRecord[]:
// { id: string; timestamp: number; payload: Record<string, unknown> }
Procedural Memory (learned patterns)
// Store a procedure — embedding is required so the pattern is recallable,
// and the generated point id is returned
const procId = await memory.learnProcedure('procedures', {
  name: 'deploy-to-prod',
  steps: ['Run tests', 'Build artifacts', 'Push to registry', 'Deploy'],
  embedding: procedureEmbedding,
  metadata: { lastUsed: Date.now() }
});

// Find matching procedures
const procs = await memory.recallProcedures('procedures', queryEmbedding, 3);
Deleting memories
// Works for facts, events, and procedures — returns true if a point was removed
await memory.deleteMemory('procedures', procId);

dimension is advisory. db.agentMemory({ dimension }) records a hint you can read back via memory.dimension, but it does not create or size any collection — the collection's own dimension (set at createCollection) governs storage and search, and your embeddings must match it.

TTL & snapshots. Durable per-point TTL (in seconds) is available from this SDK: memory entries are regular points, so db.setTtlDurable(collection, pointId, ttlSeconds) expires a fact/event/procedure durably over REST. The subsystem-namespaced TTL helpers (set_semantic_ttl, store_with_ttl, auto_expire) and versioned snapshots remain embedded-only (Rust and Python bindings) and are not exposed over REST. See the API-availability table in the Agent Memory guide.

Error Handling

All error classes extend VelesDBError and include a code property for programmatic handling.

import {
  VelesDBError,
  ValidationError,
  ConnectionError,
  NotFoundError,
  BackpressureError
} from '@wiscale/velesdb-sdk';

try {
  await db.search('nonexistent', queryVector);
} catch (error) {
  if (error instanceof NotFoundError) {
    console.log('Collection not found');
  } else if (error instanceof ValidationError) {
    console.log('Invalid input:', error.message);
  } else if (error instanceof ConnectionError) {
    console.log('Server unreachable:', error.message);
  } else if (error instanceof BackpressureError) {
    console.log('Server overloaded, retry later');
  }
}

Exports

Everything is importable from the package root:

import {
  // Client
  VelesDB,
  AgentMemoryClient,

  // Embedding helper
  OpenAIEmbedder,
  type Embedder,
  type OpenAIEmbedderOptions,

  // Backends (advanced: use VelesDB client instead)
  WasmBackend,
  RestBackend,

  // Query builder
  VelesQLBuilder,
  velesql,
  type RelDirection,
  type RelOptions,
  type NearVectorOptions,
  type FusionOptions,

  // Error classes
  VelesDBError,
  ValidationError,
  ConnectionError,
  NotFoundError,
  BackpressureError,

  // Types (selected)
  type VelesDBConfig,
  type CollectionConfig,
  type VectorDocument,
  type SearchOptions,
  type SearchQuality,
  type SearchResult,
  type SparseVector,
  type MultiQuerySearchOptions,
  type GraphEdge,
  type AddEdgeRequest,
  type TraverseRequest,
  type TraverseResponse,
  type RelateRequest,
  type RelateResponse,
  type RelationsResponse,
  type QueryApiResponse,
  type AgentMemoryConfig,
  type SemanticEntry,
  type EpisodicEvent,
  type EpisodicRecord,
  type ProceduralPattern,
} from '@wiscale/velesdb-sdk';

Performance Tips

  1. Use upsertBatch() instead of repeated upsert() calls
  2. Reuse Float32Array buffers for query vectors when possible
  3. Use WASM backend for browser apps (zero network latency)
  4. Use searchIds() when you only need IDs and scores (skips payload transfer)
  5. Use streamInsert() for high-throughput ingestion with backpressure handling
  6. Pre-initialize the client at app startup (await db.init())
  7. Tune HNSW with hnsw: { m: 16, efConstruction: 200 } for higher recall

License

Licensed under the VelesDB Core License 1.0 (source-available). The SDK bundles the VelesDB WASM engine and is governed by the Core License.

VelesDB Core and Server are licensed under VelesDB Core License 1.0 (source-available).

Keywords

velesdbvector-databaseembeddingsraglocal-firstai-memorysemantic-searchsqlite-alternativewasmtypescriptlangchainllm