chroma

Name: chroma
Availability: InStock
Author: DoanNgocCuong

Manage vector embeddings and metadata for semantic search and RAG.

Updated Aug 23, 2026

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

Ready to run

npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill chroma-doanngoccuong

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026/tree/main/.claude/skills/chroma
Command: npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill chroma-doanngoccuong

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust, open-source solution for managing and querying vector embeddings, essential for building AI applications with memory and enabling efficient semantic search.

Core Features & Use Cases

Vector Storage: Store embeddings along with associated metadata.
Vector & Full-Text Search: Perform both similarity searches on embeddings and traditional text searches.
Metadata Filtering: Filter search results based on associated metadata.
Use Case: Integrate Chroma into a Retrieval-Augmented Generation (RAG) system to allow an LLM to access and retrieve relevant information from a large corpus of documents for more informed responses.

Quick Start

Install Chroma by running pip install chromadb.

Frequently Asked Questions about chroma

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

FAQPage Schema

How do I store vector embeddings and metadata for semantic search in AI applications?▼

You can store vector embeddings alongside metadata for semantic search by creating collections and adding documents with a simple API. This Skill manages vector storage and enables efficient similarity retrieval for AI applications.

Can I use sentence-transformers to generate default embeddings for a vector database?▼

Yes, this Skill utilizes sentence-transformers to generate default embeddings for the vector database. It also offers integrations with OpenAI and HuggingFace embedding functions for customized vector generation.

How do I filter vector search results using metadata in a RAG system?▼

You can filter vector search results by applying metadata filters when querying the collection. This allows precise retrieval-augmented generation by narrowing down documents based on specific metadata attributes.

Does this vector database support both local development and production clusters?▼

Yes, the vector database supports both local development and production clusters. It provides a simple API to manage collections and query documents whether running locally or at production scale.

What is the best way to perform full-text and similarity searches on vector embeddings?▼

The best way to perform full-text and similarity searches on vector embeddings is using this Skill's integrated search capabilities. It supports both traditional text searches and similarity searches on stored embeddings.

Do I need to install chromadb and sentence-transformers to enable AI memory?▼

Yes, you need to install chromadb and sentence-transformers as dependencies to enable AI memory. These provide the core vector database functionality and default embedding generation for your applications.