chroma

Manage an open-source embedding database for semantic search and retrieval.

2|Updated Feb 4, 2026

Search Tags:#open source #semantic search #embeddings #vector database #RAG #chroma

Ready to run

npx skills add https://github.com/zhuangbiaowei/smart_bot --skill chroma-zhuangbiaowei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/chroma
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill chroma-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust, open-source solution for storing and querying vector embeddings, enabling efficient semantic search and retrieval for AI applications.

Core Features & Use Cases

Vector Storage: Store embeddings along with associated metadata.
Vector & Full-Text Search: Perform similarity searches and keyword searches.
Metadata Filtering: Filter search results based on metadata attributes.
Use Case: Integrate Chroma into a RAG pipeline to quickly retrieve relevant document chunks based on user queries, enhancing LLM responses with external knowledge.

Quick Start

Use the chroma skill to create a new collection named 'my_documents' and add the provided document with its ID.

Frequently Asked Questions about chroma

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

FAQPage Schema

How do I store embeddings and metadata for semantic search?▼

You can store embeddings and metadata for semantic search by using an open-source vector database to create collections, add vector embeddings, and attach metadata. This enables efficient similarity searches and metadata filtering for retrieval.

How do I integrate a vector database into a RAG pipeline?▼

Integrating a vector database into a RAG pipeline involves storing document chunks as embeddings with metadata, then querying the database to retrieve relevant chunks based on user queries to enhance LLM responses with external knowledge.

Can I use Chroma with Python and JavaScript clients for local development?▼

Yes, Chroma supports local development using both Python and JavaScript clients. This allows you to manage your embedding database, perform vector and full-text searches, and filter results directly within your preferred development environment.

Do I need sentence-transformers to generate embeddings for my vector database?▼

Yes, sentence-transformers is required for embedding generation. It works alongside the chromadb dependency to process text into vector embeddings, which are then stored and queried within the database for semantic search applications.

What is the best way to perform full-text and similarity searches on stored embeddings?▼

The best way to perform full-text and similarity searches on stored embeddings is to use an open-source embedding database that natively supports both vector similarity matching and keyword search, while allowing metadata filtering on the results.

Why use an open-source vector database for AI applications instead of other solutions?▼

Using an open-source vector database for AI applications provides a robust solution for storing and querying vector embeddings, facilitating efficient semantic search and retrieval for RAG pipelines without vendor lock-in.