skills.rest
nadicodeai
nadicodeaiOFFICIAL
Explore all skills by this author →
WebsiteGitHub

chroma

Index and search embeddings locally with a four-function vector store API.

Updated Apr 9, 2026
Search Tags:#embeddings#local#semantic-search#vector-store#document-retrieval
One-click install
npx skills add https://github.com/nadicodeai/argo-agent --skill chroma-nadicodeai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/nadicodeai/argo-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/nadicodeai/argo-agent --skill chroma-nadicodeai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Open-source vector database for storing embeddings and metadata, enabling fast vector and full-text search with local control and privacy.

Core Features & Use Cases

  • Simple 4-function API to add, store, query, and filter embeddings with metadata.
  • Ideal for RAG workflows, document retrieval, and notebook-to-production deployment while keeping data in-house.
  • Use Case: Build a private search engine over internal documents and datasets without relying on external services.

Quick Start

Index embeddings locally with a minimal setup and start performing fast similarity searches.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store and query embeddings locally for semantic search?▼

A self-hosted vector database enables local document retrieval by storing embeddings and metadata. It provides a simple four-function API to add, query, and filter data while keeping your information in-house.

Can I build a RAG workflow without relying on external services?▼

Yes, you can build RAG workflows without external services by using a self-hosted vector database. This allows you to index and search embeddings locally, ensuring data privacy and supporting offline development from notebooks to production.

Does this local vector store work for both notebook development and production apps?▼

This self-hosted vector database supports both notebook development and production applications. It allows you to index and search embeddings locally, ensuring privacy and offline capability across your RAG workflows.

What is the best way to build a private search engine over internal documents?▼

The best way to build a private search engine over internal documents is using a self-hosted vector database. It performs fast vector and full-text searches on stored embeddings while keeping all data in-house.

How do I filter document retrieval results using metadata?▼

You filter document retrieval results using the management function in the vector store API. This allows you to add, query, and filter embeddings by their associated metadata to refine search outputs.

Related Skills

Explore More Data & Analytics Skills→
lewisperez999lewisperez999

vector-embeddings

Generate text embeddings with OpenAI and search by cosine similarity in Upstash Vector or pg-vector.

Community
Intermediate
zilliztechzilliztech
3

multi-hop-rag

Decompose questions, retrieve across documents, and synthesize cited answers.

Official
Advanced
kivo360kivo360
1

moai-essentials-refactor

Transform domain knowledge into vector embeddings for semantic retrieval.

Community
Intermediate
jason660519jason660519

qmd

Search local notes and transcripts with hybrid BM25, vector, and reranking retrieval.

Community
Intermediate