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chroma

Manage an open-source embedding database for vector and full-text search.

Updated Mar 12, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#data storage#chroma
One-click install
npx skills add https://github.com/GarrettRoi/open-manus --skill chroma-garrettroi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/GarrettRoi/open-manus/tree/main/skills/mlops/vector-databases/chroma
Command: npx skills add https://github.com/GarrettRoi/open-manus --skill chroma-garrettroi

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 vector database solution for AI applications, enabling efficient storage and retrieval of embeddings and associated metadata.

Core Features & Use Cases

  • Vector Storage: Store and manage high-dimensional vector embeddings.
  • Metadata Filtering: Filter search results based on associated metadata.
  • Semantic Search: Perform similarity searches for RAG, document retrieval, and more.
  • Use Case: Integrate Chroma into a RAG pipeline to store document embeddings, allowing your LLM to retrieve relevant context for generating more accurate and informed responses.

Quick Start

Use the chroma skill to create a new collection named 'my_documents' and add the provided text documents with their corresponding IDs and metadata.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store and retrieve document embeddings for a RAG application?▼

Vector storage for RAG involves saving high-dimensional embeddings and metadata in a database to perform semantic similarity searches. This allows your LLM to retrieve relevant document context and generate more informed responses.

What is the best way to perform semantic search with metadata filtering?▼

Semantic search with metadata filtering requires an embedding database that supports vector and full-text search alongside metadata attributes. This combination narrows similarity results by specific document properties for precise retrieval.

Can I use sentence-transformers to generate embeddings for my vector database?▼

Yes, sentence-transformers can generate embeddings for a vector database. The generated high-dimensional vectors are stored alongside metadata to enable semantic similarity searches and document retrieval.

Does this open-source vector database scale from local development to production?▼

Yes, this open-source vector database scales from local development to production clusters. It provides a simple API to manage embeddings and metadata, accommodating application growth from prototyping to full deployment.

How do I add text documents to a collection for similarity search?▼

Adding text documents to a collection for similarity search involves creating a named collection and inserting text with corresponding IDs and metadata. This structures the data for vector and full-text search queries.

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