chroma

Store embeddings and metadata locally for fast semantic search.

150|25|Updated Apr 20, 2026

Search Tags:#RAG #offline #open-source #embedding #semantic-search #vector-database #metadata-filtering

Ready to run

npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill chroma-devsoul2026

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/Devsoul2026/Hermes-One-Click/tree/main/hermes-agent/optional-skills/mlops/chroma
Command: npx skills add https://github.com/Devsoul2026/Hermes-One-Click --skill chroma-devsoul2026

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Open-source embedding database that simplifies storing embeddings and metadata while enabling fast vector and full-text search with metadata filtering. It scales from notebooks to production environments and supports RAG workflows and document retrieval.

Core Features & Use Cases

Self-hosted embedding store with a simple 4-function API for easy integration.
Efficient vector search, metadata filtering, and cross-application sharing across notebooks and apps.
Use cases include building RAG pipelines, document retrieval systems, and offline/local development.

Quick Start

Install chroma, create or load a collection, and start performing similarity searches.

Frequently Asked Questions about chroma

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

FAQPage Schema

How do I store embeddings locally for RAG pipelines without a cloud dependency?▼

Store embeddings locally by creating a collection to hold vectors and metadata. This self-hosted vector database enables fast semantic search and retrieval-augmented generation workflows entirely offline.

Can I filter semantic search results using metadata?▼

Yes, semantic search results can be filtered using metadata. The database stores both embeddings and metadata together, allowing you to perform efficient vector searches combined with specific metadata filtering.

What is the best way to build a document retrieval system that scales from notebooks to production?▼

Use a simple 4-function API to store document embeddings and metadata. This open-source vector database scales seamlessly from local notebook development to production environments for fast document retrieval.

Does this self-hosted vector database work for offline development?▼

Yes, this self-hosted vector database fully supports offline and local development. You can persist embeddings locally and perform fast semantic search across your documents without requiring external network connections.

How do I integrate a vector database into my LLM application workflow?▼

Integrate a vector database into LLM applications by loading a collection and using the simple 4-function API. It stores embeddings and metadata locally to enable fast semantic search for retrieval-augmented generation pipelines.