Which embedding models are compatible with this Chroma skill?

The skill includes patterns for the default Sentence Transformers, OpenAI's text-embedding models, HuggingFace models, and custom-defined embedding functions.

What is ChromaDB used for?

Chroma is an AI-native open-source database used to store and search embeddings, which is the foundational technology for building RAG applications and semantic search features.

How does Chroma handle metadata?

Chroma allows you to store dictionaries of metadata alongside your embeddings, which can then be used for precise filtering during similarity searches using logical operators like $and and $or.

Can I use Chroma for local development without a cloud subscription?

Absolutely. Chroma is designed to be local-first, allowing you to run a persistent vector database directly on your machine or within a notebook.

Does this skill support both Python and JavaScript?

Yes, the skill provides implementation patterns and installation guides for both Python and JavaScript/TypeScript environments.

Chroma Vector Database

Name: Chroma Vector Database
Author: Activer007

byActiver007

Data Science & ML

Integrates ChromaDB to store embeddings and perform high-performance semantic search for AI applications.

About

The Chroma skill provides a comprehensive framework for implementing the open-source embedding database within AI-native applications. It streamlines the process of building Retrieval-Augmented Generation (RAG) systems by offering standardized patterns for storing documents, managing metadata, and performing complex vector searches. This skill covers everything from local persistence and prototyping to production-ready server configurations, including seamless integrations with popular AI frameworks like LangChain and LlamaIndex. It is ideal for developers who need a lightweight, self-hosted, and scalable solution for providing LLMs with long-term memory and domain-specific knowledge.

Key Features

Support for multiple embedding providers including OpenAI, HuggingFace, and Sentence Transformers.
Integration templates for LangChain and LlamaIndex retrieval workflows.
Standardized CRUD operations for managing vector embeddings and document metadata.
Local persistent storage and remote server-mode configuration patterns.
Advanced semantic search capabilities with built-in metadata filtering.
0 GitHub stars

Use Cases

Implementing semantic search engines over large document collections or knowledge bases.
Building RAG (Retrieval-Augmented Generation) applications with local or cloud-based storage.
Developing AI agents with persistent memory for long-running conversational contexts.

About

Key Features

Support for multiple embedding providers including OpenAI, HuggingFace, and Sentence Transformers.
Integration templates for LangChain and LlamaIndex retrieval workflows.
Standardized CRUD operations for managing vector embeddings and document metadata.
Local persistent storage and remote server-mode configuration patterns.
Advanced semantic search capabilities with built-in metadata filtering.
0 GitHub stars

Use Cases

Implementing semantic search engines over large document collections or knowledge bases.
Building RAG (Retrieval-Augmented Generation) applications with local or cloud-based storage.
Developing AI agents with persistent memory for long-running conversational contexts.