skills.rest
zhouboyu-xreal
zhouboyu-xrealCOMMUNITY
Explore all skills by this author →
GitHub

chroma

Store, search, and manage embeddings and metadata for AI applications.

Updated Jun 9, 2026
Search Tags:#open source#semantic search#embeddings#vector database#document retrieval
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill chroma-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill chroma-zhouboyu-xreal

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 solves the problem of efficiently storing, searching, and managing embeddings and metadata for AI applications, making it ideal for semantic search, RAG applications, and document retrieval.

Core Features & Use Cases

  • Vector Database: Store embeddings and metadata, perform vector and full-text search, and filter by metadata.
  • Open Source: Best for local development and open-source projects.
  • Scales from Notebooks to Production: Use for semantic search, RAG applications, or document retrieval.
  • Use Case: Imagine you are building a RAG application that requires quick access to a large corpus of documents. Chroma can be used to store and index these documents, allowing for fast retrieval and analysis.

Quick Start

Install Chroma and create a new collection to start storing and querying embeddings.

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 embeddings for a RAG application?▼

To store and search embeddings for a RAG application, you can use an open-source embedding database like Chroma to efficiently index documents, perform semantic vector search, and filter by metadata for fast retrieval and analysis.

What is the best way to manage document retrieval with semantic search locally?▼

The best way to manage document retrieval with semantic search locally is using an open-source embedding database that scales from notebooks to production, allowing you to store embeddings and perform vector search efficiently.

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

Yes, you can use sentence-transformers to generate embeddings for your vector database. Chroma integrates with sentence-transformers to compute and store embeddings, enabling efficient semantic search and document retrieval.

Does Chroma support metadata filtering alongside vector search?▼

Yes, Chroma supports metadata filtering alongside vector search. It allows you to store embeddings and metadata together, enabling you to perform vector and full-text search while filtering results by specific metadata attributes.

How does an open-source vector database scale from local notebooks to production?▼

An open-source vector database scales from local notebooks to production by providing a consistent interface for storing and querying embeddings. Chroma is designed for local development and open-source projects, making it suitable for production environments.

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
Kingly-AgencyKingly-Agency

rag-implementation

Build RAG pipelines that ground LLM responses with retrieved external knowledge.

Official
Advanced
dotlab-hqdotlab-hq

langchain-rag

Build RAG pipelines that ingest, chunk, embed, and retrieve documents for LLMs.

Official
Intermediate
Orchestra-ResearchOrchestra-Research
11.5k

pinecone

Manage a fully automated vector database with hybrid search and metadata filtering for AI applications.

Official
Advanced