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chroma

Manage vector collections and perform similarity searches with metadata filtering.

Updated Jul 13, 2026
Search Tags:#database#semantic search#embeddings#rag#vector database#chroma
One-click install
npx skills add https://github.com/zeronx798/demo-hermes-agent --skill chroma-zeronx798
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/zeronx798/demo-hermes-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/zeronx798/demo-hermes-agent --skill chroma-zeronx798

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of providing long-term, semantic memory to AI agents by enabling efficient storage and retrieval of embeddings and metadata.

Core Features & Use Cases

  • Vector Database Management: Create, update, and delete collections of document embeddings.
  • Semantic Search: Perform similarity searches to retrieve contextually relevant information for RAG applications.
  • Metadata Filtering: Narrow down search results using complex logical filters to ensure high-precision data retrieval.

Quick Start

Use the chroma skill to create a new collection named research-docs and add the provided text documents to it for future semantic retrieval.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I add semantic memory to my AI agent for RAG applications?▼

To add semantic memory for RAG applications, you use a vector database to store and retrieve document embeddings. This skill provides long-term memory by enabling efficient storage and similarity searches of embeddings and metadata.

What's the best way to perform similarity searches with metadata filtering?▼

The best way to perform similarity searches with metadata filtering is to use a vector database that supports complex logical filters. This approach narrows down search results to ensure high-precision data retrieval for your documents.

How do I manage vector collections for document retrieval?▼

You can manage vector collections by creating, updating, and deleting groups of document embeddings. This allows you to organize and retrieve raw text transformed into searchable vector representations.

Do I need sentence-transformers to generate embeddings for a vector database?▼

Yes, you need sentence-transformers or a similar embedding function to generate embeddings. This skill integrates with these functions to transform raw text into searchable vector representations for storage.

Can I use chromadb for local RAG application development?▼

Yes, you can use chromadb for local RAG application development. This skill supports both local and production environments for managing vector collections and performing semantic searches.

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