skills.rest
1thirteeng3
1thirteeng3COMMUNITY
Explore all skills by this author →
GitHub

chroma

Store, index, and query embeddings in a local vector database.

Updated Mar 24, 2026
Search Tags:#rag#embedding#semantic-search#vector-database#self-hosted#document-retrieval#chromadb
One-click install
npx skills add https://github.com/1thirteeng3/greenmoire --skill chroma-1thirteeng3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/1thirteeng3/greenmoire/tree/main/integrations/hermes-agent/skills/mlops/vector-databases/chroma
Command: npx skills add https://github.com/1thirteeng3/greenmoire --skill chroma-1thirteeng3

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Store, index, and query embeddings for AI applications in a local or self-hosted vector database.

Core Features & Use Cases

  • Embedded vector storage with metadata filtering for scalable search in local or self-hosted environments.
  • Supports LangChain, LlamaIndex integrations and full-text search capabilities for RAG workflows.
  • Use Case: Build a local/document retrieval system with metadata-based filtering.

Quick Start

Install chroma, create a collection, add documents with metadata, and run a similarity query.

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 a RAG workflow?▼

Store and query embeddings locally by creating a collection, adding documents with metadata, and running similarity queries to support RAG workflows and document retrieval.

Can I use metadata filtering for semantic search in a self-hosted vector database?▼

Metadata filtering is supported for semantic search in a self-hosted vector database, enabling scalable document retrieval by restricting similarity queries to specific metadata fields.

Does ChromaDB work with LangChain and LlamaIndex integrations?▼

LangChain and LlamaIndex integrations are supported, allowing you to connect the vector database directly into AI applications for document retrieval and RAG workflows.

What is the best way to build a document retrieval system from notebooks to production?▼

Build a document retrieval system by using an open-source embedding database that scales from local notebooks to production deployments while supporting full-text search and multiple embedding options.

Do I need an external server to run an embedding database for AI applications?▼

An external server is not required because the embedding database can run locally or self-hosted, allowing you to store, index, and query embeddings directly within your AI applications.

Why use a local vector database instead of a managed service for semantic search?▼

A local vector database provides self-hosted control over embedding storage and metadata filtering, avoiding external dependencies while supporting scalable semantic search across development and production environments.

Related Skills

Explore More Software Engineering Skills→
patornpatorn

langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

Community
Advanced
fabioc-alohafabioc-aloha
1

RAG Architecture Skill

Build RAG pipelines with document processing, chunking, embedding, and vector storage.

Community
Advanced
cxnaivecxnaive

chroma

Store and retrieve vector embeddings with metadata filtering for RAG applications.

Community
Intermediate
markwang2658markwang2658
31

qdrant-vector-search

Provide vector similarity search with REST and gRPC APIs for RAG.

Community
Advanced