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chroma

Store embeddings and metadata for semantic search and RAG pipelines.

11.5k|842|Updated Nov 3, 2025
Search Tags:#semantic search#vector database#RAG#local development#Chroma#embedding storage#open-source
One-click install
npx skills add https://github.com/zechenzhangAGI/AI-research-SKILLs --skill chroma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/zechenzhangAGI/AI-research-SKILLs/tree/main/15-rag/chroma
Command: npx skills add https://github.com/zechenzhangAGI/AI-research-SKILLs --skill chroma

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires chromadb, and includes references (resource) components.

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.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I build a semantic search application with vector embeddings?▼

Semantic search using vector embeddings stores text as numerical vectors in a database, enabling similarity-based retrieval instead of keyword matching. Chroma is an open-source embedding database that handles storage, indexing, and querying of these vectors with a simple API, letting you retrieve semantically similar documents without external cloud services.

Can I use Chroma for retrieval-augmented generation (RAG) in local development?▼

Yes. Chroma stores embeddings and metadata for RAG pipelines, supporting local and self-hosted deployment. It provides a 4-function API for adding documents, querying by similarity, and filtering by metadata—ideal for building RAG systems that keep your data private and avoid cloud dependencies.

What vector database options work for notebook prototyping without cloud infrastructure?▼

Chroma is a lightweight, open-source vector database designed for notebook-based prototyping and local development. It scales from initial experiments to production clusters, supports multiple embedding backends (Sentence Transformers, OpenAI, HuggingFace), and requires no external infrastructure setup.

Does Chroma support metadata filtering alongside vector similarity search?▼

Yes. Chroma combines vector similarity search with metadata-based filtering, allowing you to refine results by associated metadata fields. This dual-capability retrieval enhances search precision beyond pure vector similarity alone.

What embedding backends does Chroma support out of the box?▼

Chroma supports multiple embedding backends by default: Sentence Transformers, OpenAI embeddings, and HuggingFace models. You can choose the embedding model that fits your use case without rebuilding the core database logic.

Is Chroma suitable for production RAG applications or just prototyping?▼

Chroma scales from notebook prototyping to robust production clusters. Its open-source, self-hosted architecture with persistent storage and collection-level operations (add, query, get, update, delete) supports both development environments and production deployments requiring full data control.

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