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chroma

Store and search embeddings locally with a four-function vector database API.

Updated Mar 22, 2026
Search Tags:#embeddings#local#open-source#semantic-search#vector-database#chromadb
One-click install
npx skills add https://github.com/ChimeraFoundationa/Agentx --skill chroma-chimerafoundationa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/ChimeraFoundationa/Agentx/tree/main/skills/mlops/vector-databases/chroma
Command: npx skills add https://github.com/ChimeraFoundationa/Agentx --skill chroma-chimerafoundationa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Managing and querying high-dimensional embeddings and their metadata across AI workflows is complex and brittle. Chroma provides a local, self-hosted vector database that stores embeddings and metadata and offers fast similarity search to power RAG and memory-enabled apps.

Core Features & Use Cases

  • Local, self-hosted storage: persist embeddings and metadata on disk for reliability and privacy.
  • Fast vector search & filtering: perform semantic search with optional metadata filters across large collections.
  • Framework-friendly API: integrates with LangChain, LlamaIndex, and other tooling for streamlined pipelines.
  • Use cases: memory in AI agents, document retrieval, and retrieval-augmented generation (RAG) workflows.

Quick Start

Install chromadb, create a collection, and begin storing embeddings and performing semantic searches locally.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store and search embeddings locally for a RAG workflow?▼

Store and search embeddings locally using an open-source vector database with a simple four-function API. Create collections, add documents with metadata, and query for semantic similarity to power retrieval-augmented generation workflows.

What is the best way to manage high-dimensional embeddings and metadata for AI agents?▼

The best way to manage high-dimensional embeddings and metadata for AI agents is using a local, self-hosted vector database. Chroma persists embeddings on disk for reliability and offers fast similarity search to provide memory for AI agents.

Can I use this open-source vector database with LangChain and LlamaIndex?▼

Yes, you can use this open-source vector database with LangChain and LlamaIndex. Chroma offers a framework-friendly API that integrates with these tools to streamline your AI pipelines and document retrieval processes.

Does Chroma support metadata filtering during semantic search?▼

Yes, Chroma supports metadata filtering during semantic search. You can perform fast vector similarity searches across large collections while applying optional metadata filters to refine your document retrieval results.

When do I need a local, self-hosted vector database for my AI application?▼

You need a local, self-hosted vector database when managing high-dimensional embeddings across AI workflows becomes complex and brittle. Chroma provides local storage for privacy and reliability, scaling from notebooks to production environments.

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