chroma

Name: chroma
Availability: InStock
Author: ldzhhxx

Store and query embeddings with metadata in a vector database.

1|1|Updated May 9, 2026

Search Tags:#semantic search #embeddings #vector database #open-source #document retrieval

Ready to run

npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill chroma-ldzhhxx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/chroma
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill chroma-ldzhhxx

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 efficiently storing, searching, and retrieving embeddings with metadata for AI applications.

Core Features & Use Cases

Vector Database: Store embeddings and metadata for vector-based search and retrieval.
Semantic Search: Perform advanced text search with semantic understanding.
Metadata Filtering: Filter search results based on metadata properties.
Use Cases: Ideal for building RAG (retrieval-augmented generation) applications, semantic search, and document retrieval in open-source projects.

Quick Start

Use the chroma skill to add documents to your vector database and query them with embeddings or 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 semantic search?▼

To perform semantic search, you store embeddings and metadata in a vector database, then query them based on vector similarity. This skill provides an API to add documents and retrieve them using semantic understanding rather than exact keyword matches.

What is the best way to build a local RAG application with an open-source vector database?▼

Building a local RAG application requires an open-source vector database to store and retrieve document embeddings. This skill integrates with popular embedding libraries to provide seamless data manipulation and advanced retrieval capabilities for your generation tasks.

Can I filter vector database search results using metadata properties?▼

Yes, you can filter vector database search results using metadata properties. The skill supports advanced search capabilities that allow you to narrow down retrieved documents by specifying metadata filters alongside your semantic queries.

How does Chroma integrate with sentence-transformers for document retrieval?▼

Chroma integrates with sentence-transformers to generate and manage embeddings for document retrieval. This combination allows you to automatically transform text into vectors, store them in the database, and perform semantic searches without manual embedding pipelines.

Do I need a separate embedding library to use this vector database?▼

Yes, you need a separate embedding library like sentence-transformers to generate embeddings before storing them. The skill acts as the vector database for storage and retrieval, relying on these dependencies to process raw text into searchable vectors.

What are the limitations of using an open-source vector database for AI applications?▼

An open-source vector database is designed primarily for local development and open-source projects, meaning it may lack enterprise-scale distributed features. It efficiently handles metadata filtering and semantic search but might require additional infrastructure for large-scale production deployments.