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chroma

Store and retrieve embeddings and metadata for semantic search.

1|Updated Feb 17, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#document retrieval
One-click install
npx skills add https://github.com/brittaniebuffiecsu/zerogravityclaw --skill chroma-brittaniebuffiecsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/brittaniebuffiecsu/zerogravityclaw/tree/main/src/hermes-core/optional-skills/mlops/chroma
Command: npx skills add https://github.com/brittaniebuffiecsu/zerogravityclaw --skill chroma-brittaniebuffiecsu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Chroma addresses the need for a flexible and scalable vector database for AI applications, enabling semantic search, RAG (retrieval-augmented generation), and document retrieval.

Core Features & Use Cases

  • Semantic Search: Facilitates efficient searching within large document collections, leveraging embeddings for meaning-based retrieval.
  • RAG Applications: Supports RAG by providing fast access to relevant information, enhancing AI-generated content.
  • Document Retrieval: Allows for quick retrieval of documents based on embeddings and metadata, ideal for search engines and knowledge bases.
  • Use Case: Imagine a large repository of technical documents. Chroma can help you quickly find the most relevant documents for a given query, saving time and improving productivity.

Quick Start

Install Chroma and add your documents to a collection using the following command:

pip install chromadb
collection = client.create_collection("my_collection")
collection.add(
    documents=["This is document 1", "This is document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I use a vector database for semantic search and document retrieval?▼

Semantic search and document retrieval utilize a vector database like Chroma to store and retrieve embeddings and metadata, allowing efficient meaning-based search within large document collections.

How do I build RAG applications with a scalable vector database?▼

RAG applications use a scalable vector database like Chroma to provide fast access to relevant information and embeddings, enhancing AI-generated content through retrieval-augmented generation.

Does Chroma support custom embedding functions for document collections?▼

Chroma supports various embedding functions for document collections, enabling you to store and retrieve embeddings and metadata efficiently for your specific AI application requirements.

What is the best way to add documents to a collection for semantic search?▼

To add documents to a collection for semantic search, create a collection using the client and add your documents, metadatas, and ids directly to enable fast retrieval.

When do I need a vector database for AI applications?▼

You need a vector database for AI applications when you require efficient semantic search, RAG, or quick document retrieval within large repositories based on stored embeddings and metadata.

Can I use sentence-transformers with Chroma for document retrieval?▼

Chroma uses sentence-transformers as a dependency to generate embeddings, enabling efficient semantic search and document retrieval within your collections.

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