chroma

Store and retrieve embeddings with a self-hosted vector database.

Updated May 3, 2026

Search Tags:#embeddings #memory #semantic-search #vector-database #self-hosted #chromadb #LLM-integration

Ready to run

npx skills add https://github.com/JuanMS20/solviora-agent --skill chroma-juanms20

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/JuanMS20/solviora-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/JuanMS20/solviora-agent --skill chroma-juanms20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Chroma provides a self-hosted, memory-enabled vector database for building AI-powered applications, enabling fast, local embeddings storage and retrieval without relying on cloud services.

Core Features & Use Cases

Self-hosted vector store for embeddings with metadata support and full-text search.
Seamless integration with LangChain and LlamaIndex for building RAG and document retrieval pipelines.
Persistent storage and server-mode operation for production-grade deployments.

Quick Start

Install and initialize a local Chroma database and begin indexing documents and embeddings.

Frequently Asked Questions about chroma

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

FAQPage Schema

How do I set up a self-hosted vector database for local LLM memory?▼

You can use Chroma as a self-hosted vector database to store embeddings and enable semantic search locally. It offers a simple four-function API for initializing persistent storage and indexing documents without relying on external cloud services.

Can I integrate a vector store with LangChain and LlamaIndex for RAG workflows?▼

Yes, Chroma integrates seamlessly with LangChain and LlamaIndex to build Retrieval-Augmented Generation and document retrieval pipelines. This allows you to connect your local vector store directly into your existing LLM frameworks.

Does this self-hosted vector database support persistent storage and server-mode for production?▼

Yes, Chroma supports both persistent storage and server-mode operation. This allows you to scale from notebook experiments to production-grade deployments while maintaining your embeddings and metadata locally.

What is the best way to perform semantic search and full-text search on local embeddings?▼

Using a self-hosted vector store like Chroma allows you to perform semantic search and full-text search on local embeddings. It stores vectors with metadata support, enabling fast retrieval for document-heavy AI applications.

Do I need external dependencies to run a local vector database for embeddings?▼

No, Chroma operates as a self-hosted vector store with no external dependencies required. You can install it locally and immediately begin indexing documents and embeddings using its core API.