chroma

What problem does it solve?

This Skill provides an easy-to-use, flexible, and open-source database solution for storing embeddings and their associated metadata. It simplifies the development of AI applications like RAG and semantic search, especially for local prototyping and self-hosted projects.

Core Features & Use Cases

• Open-Source & Self-Hosted: Gain full control over your data and infrastructure with an Apache 2.0 licensed solution, ideal for local development and privacy-sensitive applications.
• Simple 4-Function API: Interact with a straightforward API for vector similarity search, full-text search, and metadata filtering.
• Metadata Filtering: Enhance search relevance by filtering results based on associated metadata, allowing for more precise retrieval.
• Scalable: Scales seamlessly from notebook-based prototyping to robust production clusters.
• Use Case: Build a RAG application for your local documents, allowing semantic search and retrieval without relying on external cloud services, or develop a personalized recommendation engine that stores user preferences as embeddings.

Quick Start

Create a Chroma client, then create a collection named "my_collection". Add two sample documents with metadata and IDs, then query the collection for relevant results.