Query vectors

Querying an index, or vector search, enables you to search an index by providing an input vector and returning the nearest vectors based on the configured distance metric.

Optionally, you can apply metadata filters or a namespace to narrow the vector search space.

Example query

To pass a vector as a query to an index, use the query() method on the index itself.

A query vector is either an array of JavaScript numbers, 32-bit floating point or 64-bit floating point numbers: number[], Float32Array, or Float64Array. Unlike when inserting vectors, a query vector does not need an ID or metadata.

// query vector dimensions must match the Vectorize index dimension being queried
let queryVector = [54.8, 5.5, 3.1, ...];
let matches = await env.YOUR_INDEX.query(queryVector);

This would return a set of matches resembling the following, based on the distance metric configured for the Vectorize index. Example response with cosine distance metric:

{
  "count": 5,
  "matches": [
    { "score": 0.999909486, "id": "5" },
    { "score": 0.789848214, "id": "4" },
    { "score": 0.720476967, "id": "4444" },
    { "score": 0.463884663, "id": "6" },
    { "score": 0.378282232, "id": "1" }
  ]
}

You can optionally change the number of results returned and/or whether results should include metadata and values:

// query vector dimensions must match the Vectorize index dimension being queried
let queryVector = [54.8, 5.5, 3.1, ...];
// topK defaults to 5; returnValues defaults to false; returnMetadata defaults to "none"
let matches = await env.YOUR_INDEX.query(queryVector, {
  topK: 1,
  returnValues: true,
  returnMetadata: "all",
});

This would return a set of matches resembling the following, based on the distance metric configured for the Vectorize index. Example response with cosine distance metric:

{
  "count": 1,
  "matches": [
    {
      "score": 0.999909486,
      "id": "5",
      "values": [58.79999923706055, 6.699999809265137, 3.4000000953674316, ...],
      "metadata": { "url": "/products/sku/55519183" }
    }
  ]
}

Refer to Vectorize API for additional examples.

Control over scoring precision and query accuracy

When querying vectors, you can specify to either use high-precision scoring, thereby increasing the precision of the query matches scores as well as the accuracy of the query results, or use approximate scoring for faster response times. Using approximate scoring, returned scores will be an approximation of the real distance/similarity between your query and the returned vectors.

High-precision scoring is enabled by setting returnValues: true on your query; this tells Vectorize to fetch and use the original vector values for your matches, which enables the computation of exact scores of matches, increasing the accuracy of the results.

Workers AI

If you are generating embeddings from a Workers AI text embedding model, the response type from env.AI.run() is an object that includes both the shape of the response vector - e.g. [1,768] - and the vector data as an array of vectors:

interface EmbeddingResponse {
  shape: number[];
  data: number[][];
}

let userQuery = "a query from a user or service";
const queryVector: EmbeddingResponse = await env.AI.run(
  "@cf/baai/bge-base-en-v1.5",
  {
    text: [userQuery],
  },
);

When passing the vector to the query() method of a Vectorize index, pass only the vector embedding itself on the .data sub-object, and not the top-level response.

For example:

let matches = await env.TEXT_EMBEDDINGS.query(queryVector.data[0], { topK: 1 });

Passing queryVector or queryVector.data will cause query() to return an error.

OpenAI

When using OpenAI’s JavaScript client API ↗ and Embeddings API ↗, the response type from embeddings.create is an object that includes the model, usage information and the requested vector embedding.

const openai = new OpenAI({ apiKey: env.YOUR_OPENAPI_KEY });

let userQuery = "a query from a user or service";

let embeddingResponse = await openai.embeddings.create({
  input: userQuery,
  model: "text-embedding-ada-002",
});

Similar to Workers AI, you will need to provide the vector embedding itself (.embedding[0]) and not the EmbeddingResponse wrapper when querying a Vectorize index:

let matches = await env.TEXT_EMBEDDINGS.query(embeddingResponse.embedding[0], {
  topK: 1,
});