small-world-rs

small-world-rs is an HNSW vector index written in Rust.

Features

• Fast, accurate and easy to implement
• Choose your precision (16 or 32 bit floats)
• Choose your distance metric
  • Supports cosine distance (recommended for text) and euclidean distance (recommended for images)
• Serialize and deserialize for persistence

Example

See the text-embeddings example for a simple example of how to use small-world-rs to perform semantic search over a set of text embeddings.

Basically, it works like this:

1. Get your embeddings, be that from OpenAI, Ollama, or wherever
2. Create a World with World::new or World::new_from_dump
3. Insert your vectors into the world with world.insert_vector
4. Perform a search with world.search
5. Dump the world with world.dump to save for later

What config values should I use?

Key Parameters:

• m: Connections per layer

  • Recommended: 16-64
  • Sweet spot: 32
  • Higher values increase recall but consume more memory

• ef_construction: Construction-time exploration factor

  • Recommended: 100-500
  • Trade-off: Higher values = better recall but slower build time
  • Rule of thumb: 2-4× your target ef_search

• ef_search: Query-time exploration factor

  • Recommended: 50-150
  • Adjustable at search time
  • Higher values increase accuracy but slow down search
  • Tune based on recall requirements