Instructions

Intake

Do not block on a long questionnaire. Ask only for details that are missing and required to choose the right path:

Dense only or hybrid search
Whether CHROMA_API_KEY, CHROMA_TENANT, and CHROMA_DATABASE are already configured
Existing embedding choice, if any

If the user has no embedding preference, default to Chroma Cloud Qwen. If hybrid search is required, use Schema() and Search(). If the task is narrow, such as fixing an existing query, reviewing code, or answering an API question, proceed with the repo context instead of forcing intake.

What to validate

Correct client import (CloudClient vs Client)
Environment variables are set for Cloud deployments
Embedding function package is installed when the selected TypeScript embedding requires one
Schema() and Search() are only used for Cloud workflows
Important: get_or_create_collection() accepts either an embedding_function OR a schema, but not both. Use schema when you need multiple indexes, hybrid search, or sparse embeddings; use embedding_function for simple dense-only search.

Quick Start

Use the CLI topic to authenticate and write Cloud credentials:

chroma login
chroma db create <db_name>
chroma db connect <db_name> --env-file

Then create a CloudClient and choose the API based on the search mode:

import { CloudClient } from 'chromadb';

const client = new CloudClient();
const collection = await client.getOrCreateCollection({ name: 'my_collection' });

Use collection.query() for dense-only search. Use Schema() plus Search() only when the user needs hybrid retrieval, multiple indexes, or more expressive ranking/query composition.

Cloud Guidance

Collections are the main isolation boundary in Chroma Cloud, and metadata is the main filtering mechanism inside a collection. Reach for Schema() only when you need explicit dense+sparse or multi-index configuration, and reach for Search() only when query() is not expressive enough.

Learn More

If you need more detailed information about Chroma beyond what's covered in this skill, fetch Chroma's llms.txt for comprehensive documentation: https://docs.trychroma.com/llms.txt

Available Topics

Typescript

Chroma Regex Filtering - Learn how to use regex filters in Chroma queries
Query and Get - Query and Get Data from Chroma Collections
Metadata - Store and query metadata, including filters and array values
Updating and Deleting - Update existing documents and delete data from collections
Schema - Schema() configures collections with multiple indexes
Chroma Cloud Qwen - Chroma's hosted Qwen embedding service
Error Handling - Handling errors and failures when working with Chroma
Collection Forking - Instantly duplicate collections using copy-on-write forking in Chroma Cloud
Search() API - An expressive and flexible API for doing dense and sparse vector search on collections, as well as hybrid search

Python

Chroma Regex Filtering - Learn how to use regex filters in Chroma queries
Query and Get - Query and Get Data from Chroma Collections
Metadata - Store and query metadata, including filters and array values
Updating and Deleting - Update existing documents and delete data from collections
Schema - Schema() configures collections with multiple indexes
Chroma Cloud Qwen - Chroma's hosted Qwen embedding service
Error Handling - Handling errors and failures when working with Chroma
Collection Forking - Instantly duplicate collections using copy-on-write forking in Chroma Cloud
Search() API - An expressive and flexible API for doing dense and sparse vector search on collections, as well as hybrid search

General

Data Model - An overview of how Chroma stores data
Integrating Chroma into an existing system - Guidance for adding Chroma search to an existing application
Chroma CLI - Getting started with the Chroma CLI for Chroma Cloud authentication and database management
Caching Collection References - Reduce repeated collection lookup requests in high-traffic Chroma Cloud applications
Quotas and Limits - Chroma Cloud quotas and request limits

chroma-cloud