chroma

Name: chroma
Availability: InStock
Author: mesferali

Store embeddings and perform semantic searches with metadata filtering.

Updated Jan 12, 2026

Search Tags:#semantic search #embeddings #vector database #RAG #AI applications #chroma

Ready to run

npx skills add https://github.com/MesferAli/XCircle --skill chroma-mesferali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/chroma
Command: npx skills add https://github.com/MesferAli/XCircle --skill chroma-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a local, open-source solution for storing and searching vector embeddings, enabling AI applications to understand and retrieve information based on semantic meaning rather than just keywords.

Core Features & Use Cases

Vector Storage: Store embeddings generated by AI models.
Semantic Search: Perform similarity searches to find conceptually related information.
Metadata Filtering: Filter search results based on associated metadata.
Use Case: Powering RAG (Retrieval-Augmented Generation) applications by providing relevant context to LLMs, enabling intelligent document retrieval, and building AI-powered search engines.

Quick Start

Use the chroma skill to create a new collection named 'my_documents' and add the provided text documents with their associated 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 search vector embeddings for semantic search?▼

To store and search vector embeddings for semantic search, you need a vector database that manages AI-generated embeddings and performs similarity searches. This Skill provides an open-source solution to store embeddings with metadata and retrieve conceptually related information.

What is the best way to provide relevant context to LLMs for RAG applications?▼

The best way to provide relevant context to LLMs for RAG applications is using an AI-native embedding database. It retrieves semantically relevant documents based on vector searches, supplying the fetched context directly to the LLM to augment its generation process.

Can I filter vector database search results by metadata?▼

Yes, you can filter vector database search results by metadata. This Skill supports storing associated metadata alongside embeddings, allowing you to perform full-text searches and apply metadata filters to refine the retrieved documents.

Does this semantic search database scale from local development to production?▼

Yes, this semantic search database scales from local development to production clusters. It is designed to manage an open-source, AI-native embedding database that supports applications requiring semantic search and retrieval at various scales.

Do I need sentence-transformers to use this vector database?▼

Yes, you need sentence-transformers to use this vector database. It is a required dependency used alongside chromadb to generate the vector embeddings from text documents that are then stored, managed, and searched within the database.