One-click install
npx skills add https://github.com/gqf2008/hermez-ai --skill chroma-gqf2008
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/gqf2008/hermez-ai/tree/main/skills/mlops/chroma
Command: npx skills add https://github.com/gqf2008/hermez-ai --skill chroma-gqf2008

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Open-source, self-hosted embedding database for AI applications to store embeddings and metadata, enabling efficient vector and full-text search with metadata-based filtering.

Core Features & Use Cases

  • Store embeddings and associated metadata for AI tasks.
  • Perform vector and full-text search with fast retrieval and filters.
  • Use cases include semantic search, RAG workflows, and document retrieval in local/development setups.
  • Lightweight, scalable API suitable for notebooks to production clusters.

Quick Start

Install chromadb and create a collection to run a basic similarity search.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store embeddings and metadata for a local RAG workflow?

You can store embeddings and metadata for local RAG workflows using a self-hosted vector database that manages document retrieval and metadata filtering efficiently.

What is the best way to run semantic search on my own documents?

The best way to run semantic search on your own documents is using a self-hosted embedding database that supports vector search and metadata filtering across local setups.

Can I use a vector database for document retrieval in production clusters?

Yes, you can use this lightweight, scalable vector database for document retrieval in production clusters, as it transitions smoothly from notebooks to production environments.

Does a self-hosted embedding database support full-text search?

A self-hosted embedding database can support both fast vector and full-text search, allowing you to retrieve relevant documents using multiple search methods.

How does metadata filtering work in vector search?

Metadata filtering in vector search works by storing associated metadata alongside embeddings, allowing you to apply specific filters to narrow down your document retrieval results.

Do I need a complex API to manage a local vector store?

No, you do not need a complex API to manage a local vector store, as this solution exposes a simple four-function API to handle embeddings and search operations.