Documentation Index
Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt
Use this file to discover all available pages before exploring further.
Chroma also provides a convenient wrapper around VoyageAI’s embedding API. This embedding function runs remotely on VoyageAI’s servers, and requires an API key. You can get an API key by signing up for an account at VoyageAI.
This embedding function relies on the voyageai python package, which you can install with pip install voyageai.import chromadb.utils.embedding_functions as embedding_functions
voyageai_ef = embedding_functions.VoyageAIEmbeddingFunction(api_key="YOUR_API_KEY", model_name="voyage-3-large")
voyageai_ef(input=["document1","document2"])
// npm install @chroma-core/voyageai
import { VoyageAIEmbeddingFunction } from "@chroma-core/voyageai";
const embedder = new VoyageAIEmbeddingFunction({
apiKey: "apiKey",
modelName: "model_name",
});
// use directly
const embeddings = embedder.generate(["document1", "document2"]);
// pass documents to query for .add and .query
const collection = await client.createCollection({
name: "name",
embeddingFunction: embedder,
});
const collectionGet = await client.getCollection({
name: "name",
embeddingFunction: embedder,
});
Multilingual model example
voyageai_ef = embedding_functions.VoyageAIEmbeddingFunction(
api_key="YOUR_API_KEY",
model_name="voyage-3-large"
)
multilingual_texts = [
'Hello from VoyageAI!', 'مرحباً من VoyageAI!!',
'Hallo von VoyageAI!', 'Bonjour de VoyageAI!',
'¡Hola desde VoyageAI!', 'Olá do VoyageAI!',
'Ciao da VoyageAI!', '您好，来自 VoyageAI！',
'कोहिअर से VoyageAI!'
]
voyageai_ef(input=multilingual_texts)
