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chroma

Store embeddings and metadata in an open-source vector database.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Chroma addresses the challenge of building large language model (LLM) applications that require persistent memory. It provides a solution for storing embeddings and metadata, enabling vector and full-text search, and filtering by metadata, without the need for a cloud provider.

Core Features & Use Cases

  • Open-Source Vector Database: Chroma is an open-source vector database that stores embeddings and metadata. It supports vector and full-text search, and filtering by metadata.
  • Local/Self-Hosted: Ideal for local development and open-source projects, with simple API for integration.
  • Semantic Search: Use Chroma for semantic search over documents, RAG (retrieval-augmented generation) applications, or document retrieval.
  • Use Case: Imagine you are developing an AI application that requires storing and querying a large collection of documents. Chroma can be used to store the embeddings and metadata of these documents, allowing for efficient search and retrieval.

Quick Start

Use the chroma skill to create a new collection and add documents with metadata.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I build LLM applications with persistent memory using an open-source vector database?▼

You can build LLM applications with persistent memory using Chroma, an open-source vector database that stores embeddings and metadata locally. It enables efficient document retrieval without relying on a cloud provider.

How do I perform semantic search over documents for a local RAG application?▼

To perform semantic search for RAG applications locally, use Chroma to store document embeddings and metadata. It supports vector and full-text search, allowing efficient retrieval of relevant documents from your collection.

Can I filter vector search results by metadata in a local AI project?▼

Yes, you can filter vector search results by metadata in local AI projects using Chroma. It stores both embeddings and metadata, enabling precise filtering alongside vector and full-text search operations.

What is the best way to store embeddings for local development without a cloud provider?▼

The best way to store embeddings locally without a cloud provider is using Chroma. As a self-hosted, open-source vector database, it provides a simple API for integrating embedding storage into your development environment.

Do I need sentence-transformers to use an open-source vector database for document retrieval?▼

Yes, using sentence-transformers is required to generate the text embeddings needed for document retrieval. Chroma relies on these embeddings, alongside metadata, to execute semantic search and power RAG applications.

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