chroma

Store and search embeddings locally with a self-hosted vector database.

Updated Jun 19, 2026

Search Tags:#embeddings #rag #search #metadata #vector #self-hosted #document-retrieval

Ready to run

npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill chroma-anandaanugrahhandyanto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/AnandaAnugrahHandyanto/savarez_agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill chroma-anandaanugrahhandyanto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Chroma provides an open-source embedding database to store vector embeddings with metadata, enabling fast similarity search, filtering, and scalable semantic retrieval.

Core Features & Use Cases

Local/self-hosted vector store for embedding assets
Vector and metadata filtering for semantic search
RAG workflows, document retrieval, and notebook-to-production deployments
Simple 4-function API for easy integration

Quick Start

Install chromadb, create a collection, and begin indexing documents with embeddings for fast retrieval.

Frequently Asked Questions about chroma

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I store and search embeddings locally for a RAG workflow?▼

A self-hosted vector database like Chroma lets you store and search embeddings locally by creating collections, indexing documents with metadata, and running similarity queries for RAG workflows.

What is a self-hosted vector database used for in semantic search?▼

In semantic search, a self-hosted vector database stores vector embeddings with metadata to enable fast similarity matching, filtering, and scalable document retrieval across notebooks and services.

Can I filter document retrieval results by metadata in an open-source vector store?▼

Yes, an open-source vector store like Chroma supports vector and metadata filtering, allowing you to refine semantic search queries and retrieve specific document assets during similarity matching.

Does this vector database work for both notebook development and production deployments?▼

Yes, this vector database supports notebook-to-production deployments, satisfying development and production workflows for RAG, semantic search, and document retrieval across notebooks, services, and applications.

What is the best way to integrate semantic search into an existing application?▼

The best way to integrate semantic search is using a simple 4-function API to create collections, store embeddings with metadata, and perform similarity queries, ensuring easy integration into services and applications.

Why use an open-source embedding database instead of a managed service for document retrieval?▼

Using an open-source embedding database for document retrieval lets you self-host your vector assets locally, apply metadata filtering, and maintain data control without relying on external managed service providers.