Use Cases for HNSW-SQLite Library

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The HNSW-SQLite library combines the speed of Hierarchical Navigable Small World (HNSW) graphs for fast approximate nearest neighbor search with the reliability and portability of SQLite for persistent storage.

📦 Installation

npm:

npm install hnswsqlite

View on npm

- GitHub:
github.com/praveencs87/hnswsqlite

🚀 How to Use

const { HNSWSqlite } = require('hnswsqlite');

// Initialize or open an index
const index = new HNSWSqlite({
  dbPath: 'my_vectors.db',
  dimensions: 128,
  space: 'l2' // or 'cosine'
});

// Add a vector
await index.addItem('item123', [0.1, 0.2, ..., 0.128]);

// Search for nearest neighbors
const results = await index.search([0.1, 0.2, ..., 0.128], 10);
console.log(results);

// Save and close
await index.close();

For more usage examples and API details, see the API Documentation.

🔎 Use Cases

Semantic Search in Text and Documents:

Find semantically similar documents, articles, or code snippets using vector embeddings.

Image and Multimedia Retrieval:
Quickly find visually similar images or audio/video content by comparing feature vectors.

Recommendation Systems:
Suggest similar products, content, or users based on embeddings.

Anomaly Detection:
Identify outliers or unusual data points in high-dimensional datasets.

Scientific and Medical Data Analysis:
Search for similar gene sequences, patient records, or medical images.

Edge & Mobile Applications:
Use SQLite’s lightweight footprint and HNSW’s speed for on-device search and recommendation.

Knowledge Graphs & Entity Linking:
Resolve and link entities across datasets using vector similarity.

💡 Why Use HNSW-SQLite?

Persistence: Reliable storage with SQLite.
Performance: Fast, scalable nearest neighbor search.
Portability: Cross-platform, no server required.
Simplicity: Easy integration with minimal dependencies.

Links:

npm package

GitHub repository

API Documentation

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