Distributed search, reforged.
Add fast, relevant search to your app: full-text, vector, hybrid, and geo, in the same class as Elasticsearch and OpenSearch. Narsil runs as a search server, or embeds straight in your process with no network hop, all from a single npm package.
From install to first query in one file
Create an index with a typed schema, insert documents, and query with filters. The same engine runs embedded in Node.js, inside the browser, or behind the bundled HTTP server.
Elasticsearch and OpenSearch are search servers you stand up and scale. Narsil is the same class of engine, and the one codebase also embeds in your process, in Node.js, Bun, Deno, or the browser. Multi-node cluster mode is in active development.
Read the getting started guideimport { createNarsil } from '@delali/narsil'
const narsil = await createNarsil()
await narsil.createIndex('products', {
schema: {
title: 'string',
description: 'string',
price: 'number',
inStock: 'boolean',
},
language: 'english',
})
await narsil.insert('products', {
title: 'Mechanical Keyboard',
description: 'Cherry MX Brown switches with PBT keycaps',
price: 129.99,
inStock: true,
})
const results = await narsil.query('products', {
term: 'mechanical keyboard',
filters: {
fields: {
inStock: { eq: true },
price: { lte: 200 },
},
},
})Ask your data, with sources
Narsil retrieves the passages, your model writes the answer, and the matched text stays beside the response so that you can check the answer against its sources. The server-app example has this working end to end.
The Cmd+K search on this site runs on Narsil.
Does vitamin D supplementation reduce respiratory infections?
The retrieved trials report a modest protective effect, strongest in participants who started with a deficiency and took daily doses rather than large single boluses.[1][2]
Sources
...daily vitamin D reduced the risk of acute respiratory infection among all participants...
...protective effects were strongest in those with a baseline deficiency...
One dependency covers the whole engine
Keyword, vector, hybrid, and geo queries run on the same typed index, and partitioning and persistence install with them as one npm package.
- Full-text search
- BM25 ranking calibrated against the Anserini reference configuration, with typed schemas, filters, boosts, and highlighting.
- Vector search
- An HNSW index answers dense nearest-neighbour queries, with embedding adapters for local models and OpenAI-compatible APIs.
- Hybrid ranking
- Keyword and vector results fuse through reciprocal rank fusion or a linear blend, tunable per query.
- Geosearch
- Index coordinates and query by radius or bounding box alongside text and vector conditions.
- Partitions and workers
- Large indexes shard across partitions and worker threads, and every query merges the shards back into one ranked list.
- Durable persistence
- A write-ahead log, checkpoints, and portable .nrsl snapshots move indexes between Node.js, the browser, and the server.
Measured against the engines you already run
A recorded continuous-integration run scores Narsil against Elasticsearch, Meilisearch, OpenSearch, Qdrant, Typesense, and Weaviate on BEIR datasets, with pinned versions and equal memory caps.
0.6814
nDCG@10 on SciFact
The best ranking quality in the run, narrowly ahead of Elasticsearch and OpenSearch on 0.6789, and ahead again on NFCorpus at 0.3278 against their 0.3206.
958
Queries per second
Peak keyword throughput on SciFact, ahead of OpenSearch on 878 and Elasticsearch on 841, and 1,089 against their 969 and 975 on NFCorpus.
6
Engines compared
Every engine runs from a pinned image under the same memory cap, one at a time.
Start building with Narsil
The documentation walks you from installation to production configuration.