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chroma

Store and query vector embeddings with metadata filtering for semantic search.

1|Updated May 21, 2026
Search Tags:#open source#semantic search#vector database#RAG#document retrieval
One-click install
npx skills add https://github.com/blueskies1818/hermesALIone --skill chroma-blueskies1818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/optional-skills/mlops/chroma
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill chroma-blueskies1818

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Chroma solves the problem of efficiently storing and querying vector embeddings, making it ideal for semantic search, RAG (Retrieval-Augmented Generation), and document retrieval applications.

Core Features & Use Cases

  • Vector Database: Store embeddings and metadata, perform vector and full-text search, filter by metadata.
  • Open Source: Simple 4-function API, scales from notebooks to production clusters.
  • Use Cases: Semantic search, RAG applications, document retrieval, and open-source projects.

Quick Start

Install Chroma and use the following Python command to add documents to a collection:

import chromadb
client = chromadb.Client()
collection = client.create_collection(name="my_collection")
collection.add(documents=["This is document 1", "This is document 2"], metadatas=[{"source": "doc1"}, {"source": "doc2"}], ids=["id1", "id2"])

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store vector embeddings for semantic search and document retrieval?▼

You can store vector embeddings for semantic search by using a vector database like Chroma to create collections and add documents with associated metadata. This enables efficient document retrieval and filtering.

What is the best open-source vector database for RAG applications?▼

An open-source vector database for RAG applications provides a simple API to store embeddings and metadata. Chroma is designed for this exact use case, scaling seamlessly from local development notebooks to production clusters.

Do I need sentence-transformers to build a vector database for RAG?▼

Yes, building a vector database for RAG with Chroma requires both chromadb and sentence-transformers. These dependencies handle embedding generation and metadata management necessary for semantic search operations.

Can I filter vector search results by metadata in a local development environment?▼

Yes, you can filter vector search results by metadata in a local development environment. Chroma supports adding metadata to document collections and filtering by those attributes during semantic search queries.

How does a vector database handle full-text search alongside semantic search?▼

A vector database handles full-text search alongside semantic search by querying stored document collections concurrently. Chroma supports both vector search and full-text search to optimize document retrieval operations.

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