Chroma Cloud Qwen

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

Python
TypeScript

This embedding function relies on the httpx python package, which you can install with pip install httpx.

from chromadb.utils.embedding_functions import ChromaCloudQwenEmbeddingFunction, ChromaCloudQwenEmbeddingModel
import os

os.environ["CHROMA_API_KEY"] = "YOUR_API_KEY"
qwen_ef = ChromaCloudQwenEmbeddingFunction(
    model=ChromaCloudQwenEmbeddingModel.QWEN3_EMBEDDING_0p6B,
    task="nl_to_code"
)

texts = ["Hello, world!", "How are you?"]
embeddings = qwen_ef(texts)

You must pass in a model argument and task argument. The task parameter specifies the task for which embeddings are being generated. You can optionally provide custom instructions for both documents and queries.

// npm install @chroma-core/chroma-cloud-qwen

import { ChromaCloudQwenEmbeddingFunction, ChromaCloudQwenEmbeddingModel } from "@chroma-core/chroma-cloud-qwen";

const embedder = new ChromaCloudQwenEmbeddingFunction({
  apiKeyEnvVar: "CHROMA_API_KEY", // Or set CHROMA_API_KEY env var
  model: ChromaCloudQwenEmbeddingModel.QWEN3_EMBEDDING_0p6B,
  task: "nl_to_code",
});

// use directly
const embeddings = await embedder.generate(["document1", "document2"]);

// pass documents to query for .add and .query
const collection = await client.createCollection({
  name: "name",
  embeddingFunction: embedder,
});

Visit Chroma Cloud documentation for more information on available models and configuration.

Dense Embedding Models

Sparse Embedding Models

Frameworks