Cohere Embeddings (via AWS Bedrock)

When using Cohere embeddings, llm-exe will make POST requests to the AWS Bedrock endpoint for your configured region. Both Cohere Embed v3 and Embed v4 are supported through a single provider key — pick the variant via the model option.

Options

Option	Type	Default	Description
model	string	—	The Bedrock model ID (e.g., cohere.embed-english-v3, cohere.embed-multilingual-v3, cohere.embed-v4:0)
inputType	"search_document" | "search_query" | "classification" | "clustering"	"search_document"	How Cohere should prepare the embedding. Use search_document for the corpus you index and search_query for queries you embed at search time
truncate	"NONE" | "START" | "END" | "LEFT" | "RIGHT"	—	How over-length inputs are handled. START/END for v3, LEFT/RIGHT for v4. NONE returns an error if the input is too long
dimensions	number	—	Embed v4 only. Output vector size: 256, 512, 1024, or 1536. Maps to Cohere's output_dimension. Embed v3 has a fixed 1024-dim output and rejects this field — leave it unset
awsRegion	string	AWS_REGION env var	The AWS region for the Bedrock endpoint (required)
awsSecretKey	string	—	AWS secret key (if not using default credentials)
awsAccessKey	string	—	AWS access key (if not using default credentials)

Basic Usage

import { createEmbedding } from "llm-exe";

const embeddings = createEmbedding("amazon:cohere.embedding.v1", {
  model: "cohere.embed-multilingual-v3",
  awsRegion: "us-west-2",
});

const embedding = await embeddings.call("The string of text you would like as vector");
const vector = embedding.getEmbedding();
console.log(vector);
// [
//   -0.0419921875, 0.005367279052734375, 0.011688232421875,
//   -0.0096893310546875, 0.039459228515625, -0.0231781005859375,
//   // ...
// ]

Batching

Cohere accepts up to 96 texts per call. Pass an array to embed them in a single request:

const embedding = await embeddings.call([
  "RAG system design patterns",
  "Actuarial loss triangles and reserving primer",
]);

// One vector per input text
const first = embedding.getEmbedding(0);
const second = embedding.getEmbedding(1);

Search vs Indexing

For retrieval (RAG) workflows, embed your corpus with inputType: "search_document" and your queries with inputType: "search_query". Cohere prepends different special tokens for each case, which improves retrieval quality.

// At index time
const docEmbeds = createEmbedding("amazon:cohere.embedding.v1", {
  model: "cohere.embed-english-v3",
  inputType: "search_document",
});

// At query time
const queryEmbeds = createEmbedding("amazon:cohere.embedding.v1", {
  model: "cohere.embed-english-v3",
  inputType: "search_query",
});

Embed v4 Output Dimensions

Cohere Embed v4 supports variable output dimensions. Pass dimensions to pick one of 256, 512, 1024, or 1536 (default 1536 if unset). Smaller vectors trade off some retrieval quality for cheaper storage and faster nearest-neighbor lookups.

const embeddings = createEmbedding("amazon:cohere.embedding.v1", {
  model: "cohere.embed-v4:0",
  dimensions: 512,
});

Notes

• Cohere on Bedrock does not return token usage in the response, so getResult().usage.input_tokens will be 0.
• Cohere models on Bedrock are served from AWS Marketplace. The first invocation in your account must come from a user with aws-marketplace:Subscribe permissions, after which all users in the account can invoke the model.
• llm-exe is text-only; image inputs to Cohere Embed v3/v4 are not supported.