Vector Search

Overview

turbopuffer supports both approximate nearest neighbor (ANN) search for speed at scale, and exact kNN search for recall-critical workloads. Both operate over dense float vector embeddings stored in your namespace.

ANN search

ANN search uses an index to find the nearest neighbors quickly, trading a small amount of recall for significant speed gains at scale.

const results = await client.namespaces.query("my-namespace", {
  rankBy: ["vector", "ANN", queryVector],
  topK: 20,
  distanceMetric: "cosine_distance",
});

Exact kNN search

kNN performs an exhaustive search — guaranteed recall, slower on large datasets.

const results = await client.namespaces.query("my-namespace", {
  rankBy: ["vector", "kNN", queryVector],
  topK: 20,
});

Distance metrics

Auto-embedding

turbopuffer can generate embeddings automatically from a text attribute using a hosted model:

await client.namespaces.write("my-namespace", {
  schema: {
    text: {
      type: "string",
      embed: { model: "text-embedding-3-small" },
    },
  },
});

Then query using an Embed expression instead of a raw vector:

const results = await client.namespaces.query("my-namespace", {
  rankBy: ["text", "ANN", ["Embed", "my search query"]],
  topK: 10,
});