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chroma

Store, manage, and retrieve embeddings with metadata using chromadb.

Updated Apr 20, 2026
Search Tags:#rag#metadata#embedding#semantic-search#vector-database#chromadb
One-click install
npx skills add https://github.com/matthew-johnson/hermes-agent --skill chroma-matthew-johnson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/matthew-johnson/hermes-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/matthew-johnson/hermes-agent --skill chroma-matthew-johnson

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires chromadb, sentence-transformers, and includes references (resource) components.

What problem does it solve?

Storing, organizing, and efficiently retrieving high-dimensional embeddings and their metadata for AI applications with a local, self-hosted solution.

Core Features & Use Cases

  • Store embeddings with metadata and perform both vector and full-text search for rapid retrieval.
  • Simple 4-function API and scalable from notebooks to production deployments.
  • Ideal for memory-enabled AI workflows, RAG pipelines, and document retrieval in open-source projects.

Quick Start

Install chromadb, create a collection, add documents with embeddings, and run a similarity search.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store embeddings with metadata for a local RAG pipeline?▼

You can store embeddings with metadata for RAG pipelines using a local, self-hosted vector database. This allows you to manage high-dimensional vectors and perform both vector and full-text search for rapid retrieval.

What is a self-hosted vector database used for in AI applications?▼

A self-hosted vector database is used for storing, organizing, and efficiently retrieving high-dimensional embeddings. It enables memory-enabled AI workflows, semantic search, and document retrieval in open-source projects.

Do I need sentence-transformers to run semantic search workflows?▼

Yes, you need a compatible embedding function like sentence-transformers to generate vectors. The system requires both the chromadb library and an embedding model to run end-to-end semantic search pipelines.

Can I filter vector search results using metadata in a local database?▼

Yes, you can filter vector search results using metadata. The database offers strong support for metadata filtering alongside vector similarity search to refine document retrieval in AI applications.

How do I scale document retrieval from a notebook to production?▼

You can scale document retrieval from notebooks to production deployments using a simple 4-function API. This allows your RAG pipelines and memory-enabled AI workflows to grow without changing the underlying vector database structure.

When should I not use an open-source embedding database?▼

You should not use an open-source embedding database if your AI application requires managed cloud hosting rather than a local, self-hosted solution, or if you do not need to store high-dimensional vectors for semantic search.

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