Cohere

Chroma also provides a convenient wrapper around Cohere's embedding API. This embedding function runs remotely on Cohere’s servers, and requires an API key. You can get an API key by signing up for an account at Cohere.

Models	Input	Dimensionality	Context Size
embed-english-v3.0	English	1024	512 (recommended)
embed-multilingual-v3.0	Full List	1024	512 (recommended)
embed-english-light-v3.0	English	384	512 (recommended)
embed-multilingual-light-v3.0	Full List	384	512 (recommended)
embed-english-v2.0	English	4096	512 (recommended)
embed-english-light-v2.0	English	1024	512 (recommended)
embed-multilingual-v2.0	Full List	768	512 (recommended)

Basic Usage

Python

pip install cohere

from chromadb.utils import embedding_functions

embedder = embedding_functions.CohereEmbeddingFunction(
        api_key="YOUR_API_KEY")

collection = client.create_collection(
        name="cohere_ef", 
        embedding_function=embedder)

Javascript

yarn add cohere-ai

import { ChromaClient, CohereEmbeddingFunction } from 'chromadb'

const embedder = new CohereEmbeddingFunction({
    apiKey: "YOUR_API_KEY"
})

const collection = await client.createCollection({
    name: "cohere_ef", 
    embeddingFunction: embedder
})

Advanced Usage

Call directly

By passing the embedding function to a Collection, Chroma handles the embedding of documents and queries for you. However in some cases you may want to generate the embeddings outside and handle them yourself.

Python

embeddings = embedder(["document1","document2"])
# [[0.04565250128507614, 0.01611952856183052...], [0.030171213671565056, 0.007690359838306904...]]

Javascript

const embeddings = embedder.generate(["document1","document2"])
// [[0.04565250128507614, 0.01611952856183052...], [0.030171213671565056, 0.007690359838306904...]]

Using a different model

You can pass in an optional model_name argument, which lets you choose which Cohere embeddings model to use. By default, Chroma uses large model. You can see the available models under Get embeddings section here.

Multilingual model example

Python

cohere_ef  = embedding_functions.CohereEmbeddingFunction(
        api_key="YOUR_API_KEY", 
        model_name="multilingual-22-12")

multilingual_texts  = [ 'Hello from Cohere!', 'مرحبًا من كوهير!', 
        'Hallo von Cohere!', 'Bonjour de Cohere!', 
        '¡Hola desde Cohere!', 'Olá do Cohere!', 
        'Ciao da Cohere!', '您好，来自 Cohere！',
        'कोहेरे से नमस्ते!'  ]

cohere_ef(texts=multilingual_texts)

Javascript

import { CohereEmbeddingFunction } from 'chromadb'
const embedder = new CohereEmbeddingFunction("apiKey")

multilingual_texts  = [ 'Hello from Cohere!', 'مرحبًا من كوهير!', 
        'Hallo von Cohere!', 'Bonjour de Cohere!', 
        '¡Hola desde Cohere!', 'Olá do Cohere!', 
        'Ciao da Cohere!', '您好，来自 Cohere！',
        'कोहेरे से नमस्ते!'  ]

const embeddings = embedder.generate(multilingual_texts)

For more information on multilingual model you can read here.