Cohere Provider

The Cohere provider contains language and emdedding model support for the Cohere chat API.

Setup

The Cohere provider is available in the @ai-sdk/cohere module. You can install it with

pnpm add @ai-sdk/cohere

Provider Instance

You can import the default provider instance cohere from @ai-sdk/cohere:

import { cohere } from '@ai-sdk/cohere';

If you need a customized setup, you can import createCohere from @ai-sdk/cohere and create a provider instance with your settings:

import { createCohere } from '@ai-sdk/cohere';

const cohere = createCohere({
  // custom settings
});

You can use the following optional settings to customize the Cohere provider instance:

• baseURL string

  Use a different URL prefix for API calls, e.g. to use proxy servers. The default prefix is https://api.cohere.com/v2.

• apiKey string

  API key that is being sent using the Authorization header. It defaults to the COHERE_API_KEY environment variable.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

  Custom fetch implementation. Defaults to the global fetch function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.

Language Models

You can create models that call the Cohere chat API using a provider instance. The first argument is the model id, e.g. command-r-plus. Some Cohere chat models support tool calls.

const model = cohere('command-r-plus');

Example

You can use Cohere language models to generate text with the generateText function:

import { cohere } from '@ai-sdk/cohere';
import { generateText } from 'ai';

const { text } = await generateText({
  model: cohere('command-r-plus'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

Cohere language models can also be used in the streamText function (see AI SDK Core).

Model Capabilities

Embedding Models

You can create models that call the Cohere embed API using the .embedding() factory method.

const model = cohere.embedding('embed-english-v3.0');

Cohere embedding models support additional settings. You can pass them as an options argument:

const model = cohere.embedding('embed-english-v3.0', {
  inputType: 'search_document',
});

The following optional settings are available for Cohere embedding models:

• inputType 'search_document' | 'search_query' | 'classification' | 'clustering'

  Specifies the type of input passed to the model. Default is search_query.

  • search_document: Used for embeddings stored in a vector database for search use-cases.
  • search_query: Used for embeddings of search queries run against a vector DB to find relevant documents.
  • classification: Used for embeddings passed through a text classifier.
  • clustering: Used for embeddings run through a clustering algorithm.

• truncate 'NONE' | 'START' | 'END'

  Specifies how the API will handle inputs longer than the maximum token length. Default is END.

  • NONE: If selected, when the input exceeds the maximum input token length will return an error.
  • START: Will discard the start of the input until the remaining input is exactly the maximum input token length for the model.
  • END: Will discard the end of the input until the remaining input is exactly the maximum input token length for the model.

Model Capabilities