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

Ready to run

npx skills add https://github.com/1thirteeng3/greenmoire --skill chroma-1thirteeng3

💬 Prompt for ChatGPT / Claude Chat▼

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.