Vector Search

Being a vector database, Orama allows you to perform vector search natively.

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

Generating Embeddings

In this page, we will assume that you already have the embeddings for your documents.

If you don’t, you can use the Plugin Embeddings to generate them directly offline and on-device at insert and search time.

If you’re running Orama in a memory-constrained environment, you can also use the Plugin Secure Proxy to generate embeddings on the fly using the OpenAI API directly from the client-side.

Performing Vector Search

To perform vector search, you will need to use the same search method you’re already using for full-text and hybrid search, which can be imported from @orama/orama:

import { search } from "@orama/orama";

The key differences between running vector search and full-text search are:

1. Instead of searching for a term, you will need to provide a vector object to search.
2. You will need to specify the vector property you want to search on.
3. 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 to support multiple vector properties at search-time.

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

import { create, insertMultiple, search } from "@orama/orama";

const db = create({
  schema: {
    title: "string", // To make it simple, let's pretend that
    embedding: "vector[5]", // we are using a 5-dimensional vector.
  },
});

insertMultiple(db, [
  {
    title: "The Prestige",
    embedding: [0.938293, 0.284951, 0.348264, 0.948276, 0.56472],
  },
  {
    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 = search(db, {
  mode: "vector",
  vector: {
    value: [0.938292, 0.284961, 0.248264, 0.748276, 0.26472],
    property: "embedding",
  },
  similarity: 0.85, // Minimum similarity. Defaults to `0.8`
  includeVectors: true, // Defaults to `false`
  limit: 10, // Defaults to `10`
  offset: 0, // Defaults to `0`
});

Simplify it!

When running vector search using the secure proxy or embeddings plugins, you won’t need to explicitly pass the vector configuration. Just run a simple search query!

const results = search(db, {
  mode: "vector",
  term: "Videogame for little kids with a passion about ice cream",
});

The plugin will automatically convert this text to vector for you, and will also prevent you from exposing your OpenAI API key when using it on the front-end.

The returning object will be exactly the same as the one we would expect when performing full-text search:

{
  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).