Vector Search

Since v1.2.0, Orama supports vector search natively 🎉.

To perform search through vectors, you need to correctly configure your Orama schema, as described in the create page.

Performing Vector Search

To perform vector search, you will need to use a new method called searchVector, which can be imported from @orama/orama:

import { searchVector } from '@orama/orama'

The APIs are very similar to the ones you already know, but with a few differences:

Instead of searching for a term, you will need to provide a vector to search for.
You will need to specify the vector property you want to search on.
At the time of writing, you can only search through one vector property at a time. If you think that this is too limiting, please open a feature request (opens in a new tab) to support multiple vector properties at search-time.

Let's see a full example of how to perform vector search:

import { create, insertMultiple, searchVector } from '@orama/orama'
 
const db = await create({
  schema: {
    title: 'string',        // To make it simple, let's pretend that
    embedding: 'vector[5]', // we are using a 5-dimensional vector.
  }
})
 
await insertMultiple(db, [
  { title: 'The Prestige', embedding: [0.938293, 0.284951, 0.348264, 0.948276, 0.564720] },
  { title: 'Barbie',       embedding: [0.192839, 0.028471, 0.284738, 0.937463, 0.092827] },
  { title: 'Oppenheimer',  embedding: [0.827391, 0.927381, 0.001982, 0.983821, 0.294841] },
])
 
const results = await searchVector(db, {
  vector: [0.938292, 0.284961, 0.248264, 0.748276, 0.264720],
  property: 'embedding',
  similarity: 0.8,      // Minimum similarity. Defaults to `0.8`
  includeVectors: true, // Defaults to `false`
  limit: 10,            // Defaults to `10`
  offset: 0,            // Defaults to `0`
})

The returning object will be exactly the same as the one we would expect from the default search method:

{
  count: 1,
  elapsed: {
    raw: 25000,
    formatted: '25ms',
  },
  hits: [
    {
      id: '1-19238',
      score: 0.812383129,
      document: {
        title: 'The Prestige',
        embedding: [0.938293, 0.284951, 0.348264, 0.948276, 0.564720],
      }
    }
  ]
}

Since vectors can be quite large, you can also choose to not include them in the response by setting includeVectors to false (default behavior).

Searching with Orama Fields boosting