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chroma

Store and query embeddings with metadata in a local vector database.

Updated Aug 23, 2026
Search Tags:#embeddings#rag#metadata#open-source#semantic-search#vector-database#chromadb
One-click install
npx skills add https://github.com/travelinman1013/leroys-agent --skill chroma-travelinman1013
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/travelinman1013/leroys-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/travelinman1013/leroys-agent --skill chroma-travelinman1013

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Organizations building AI apps need a local, scalable way to store and query embeddings and associated metadata for memory and retrieval tasks.

Core Features & Use Cases

  • Local vector store for embeddings with metadata, enabling offline development and production-scale deployments.
  • Unified API for storing embeddings, running similarity search, and filtering by metadata.
  • Suitable for RAG pipelines, semantic search, and document retrieval from notebooks to production.

Quick Start

Install chromadb, create a collection, add documents, and run a query to verify the setup.

Frequently Asked Questions about chroma

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

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

Store and query embeddings with metadata for retrieval-augmented generation using a local, self-hosted vector database. A unified API handles semantic similarity search and metadata filtering for production-scale AI memory.

What is the best way to run a local vector database for semantic search?▼

Run a local vector database to enable offline development and production-scale semantic search. It provides a simple API to store embeddings and execute similarity queries without relying on external managed services.

Can I use sentence-transformers to generate embeddings for chromadb?▼

Yes, sentence-transformers is a required dependency for generating embeddings. The database stores these vector representations alongside associated metadata to support retrieval queries and similarity matching.

Does this self-hosted embedding store support metadata filtering for AI applications?▼

The self-hosted embedding store supports metadata filtering for AI applications. You can store document embeddings with contextual metadata and filter similarity search results based on those specific metadata attributes.

When do I need a local vector database for document retrieval?▼

You need a local vector database for document retrieval when building AI apps that require memory and self-hosted data control. It scales from notebook prototyping to production deployments for semantic search tasks.

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