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chroma

Store, index, and query vector embeddings for semantic search.

2|1|Updated May 10, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#AI#self-hosted#document retrieval
One-click install
npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill chroma-zli5460
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill chroma-zli5460

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Facilitates storing, indexing, and searching vector embeddings for AI tasks.

Core Features & Use Cases

  • Semantic search for documents and data retrieval.
  • Retrieval-Augmented Generation (RAG) workflows.
  • Open-source self-hosted vector database solution.

Quick Start

Use the chroma skill to perform semantic search over your document collection.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I perform semantic search over my document collection?▼

To perform semantic search, you use a vector database to store and query document embeddings. This Skill supports indexing large-scale embedding data and filtering to retrieve relevant documents based on semantic similarity.

What is an open-source vector database used for in AI applications?▼

An open-source vector database stores, indexes, and searches vector embeddings for AI tasks. It enables efficient document retrieval and powers Retrieval-Augmented Generation workflows by querying large-scale embedding data locally.

Can I use a self-hosted vector database for Retrieval-Augmented Generation workflows?▼

Yes, a self-hosted vector database supports Retrieval-Augmented Generation workflows. This Skill enables local deployment and AI pipeline integration to manage and query embeddings for document retrieval.

Does this semantic search tool require sentence-transformers to generate embeddings?▼

Yes, generating embeddings for semantic search requires a model like sentence-transformers. This Skill depends on sentence-transformers alongside its vector database to process and store embeddings for retrieval.

What is the best way to manage large-scale embedding data locally?▼

Managing large-scale embedding data locally is best achieved using a self-hosted vector database. This Skill provides efficient indexing, filtering, and querying capabilities for large-scale embedding data without external dependencies.

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