skills.rest
NITISH-gitbit
NITISH-gitbitCOMMUNITY
Explore all skills by this author →
GitHub

chroma

Store, search, and retrieve embeddings and metadata with a vector database.

Updated Jun 26, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#document retrieval
One-click install
npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill chroma-nitish-gitbit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill chroma-nitish-gitbit

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 challenge of managing and querying large-scale vector databases, providing a simple and scalable solution for AI applications.

Core Features & Use Cases

  • Embedding Storage: Store and manage embeddings and metadata with ease.
  • Search Capabilities: Perform vector and full-text search, filter by metadata.
  • API Access: Access a straightforward 4-function API for local and production use.
  • Use Case: Use Chroma to build semantic search applications, retrieval-augmented generation (RAG) systems, or document retrieval services.

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 store and query embeddings for a semantic search application?▼

To store and query embeddings for semantic search, you need a vector database to manage vector data and metadata. This solution provides a simple API to store embeddings and perform vector searches.

What is the best way to build a document retrieval system using Python?▼

Building a document retrieval system requires storing text and metadata for search. This solution offers a straightforward 4-function API to add documents with metadata and perform retrieval locally.

Can I use sentence-transformers to generate embeddings for a RAG application?▼

Yes, generating embeddings for RAG applications requires libraries like sentence-transformers. This solution depends on it to process text and store the resulting embeddings in the vector database.

Does this vector database support filtering search results by metadata?▼

Filtering search results by metadata is supported by this vector database. You can store metadata alongside embeddings and apply filters during the vector and full-text search process.

Do I need to install chromadb to manage large-scale vector databases?▼

Yes, installing chromadb is required to manage large-scale vector databases. This solution acts as a wrapper providing a simple, scalable interface for storage and search.

How does a vector database handle retrieval-augmented generation systems?▼

A vector database handles retrieval-augmented generation by storing document embeddings and metadata. It retrieves relevant context through vector search to feed into AI generation models.

Related Skills

Explore More Software Engineering Skills→
Quill-AgentQuill-Agent
3

chroma

Store and retrieve embeddings and metadata in a Chroma vector database.

Community
Intermediate
math-incmath-inc
1.2k

clip

Compute image-text embeddings with CLIP for zero-shot classification and retrieval.

Official
Intermediate
Greenmamba29Greenmamba29

vector-embedder

Generate and store vector embeddings for semantic search pipelines.

Community
Intermediate
HarriesHarries

chroma

Store and search embeddings with metadata in a self-hosted database.

Community
Intermediate