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chroma

Store embeddings and metadata in an open-source vector database for semantic search.

Updated May 3, 2026
Search Tags:#embeddings#rag#memory#semantic-search#local-development#vector-database#chromadb
One-click install
npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill chroma-yangel-hide
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/Yangel-hide/video-production-planner-agent/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill chroma-yangel-hide

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Store embeddings and metadata in an open-source vector database to enable fast semantic search for AI applications.

Core Features & Use Cases

  • Store embeddings with metadata for AI pipelines
  • Vector and full-text search with metadata filtering
  • Lightweight API for collections, documents, and persistence
  • Use cases: memory-enabled LLMs, RAG pipelines, document retrieval

Quick Start

Install chromadb, create a collection, and add documents with embeddings to enable retrieval.

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 in local development?▼

To store embeddings for semantic search locally, use a lightweight vector database API to create collections, add documents with embeddings, and query by similarity. This approach supports memory-enabled LLMs and on-prem deployments without external dependencies.

What is the best way to build a RAG pipeline with an open-source vector database?▼

Building a RAG pipeline with an open-source vector database involves creating collections, adding documents with embeddings and metadata, and retrieving them via similarity search. This enables fast document retrieval for AI applications across local or on-prem environments.

Can I filter vector search results using metadata in ChromaDB?▼

Yes, you can filter vector search results using metadata in ChromaDB. The database supports both vector and full-text search with metadata filtering, allowing you to refine retrieved documents based on specific attributes within your AI pipelines.

Does this vector database work for on-prem deployments?▼

Yes, this vector database works for on-prem deployments. It is designed for local development and on-prem environments, providing a lightweight API with optional data persistence for memory-enabled workflows and document retrieval.

How do I persist embeddings and collections in a local vector database?▼

To persist embeddings and collections in a local vector database, use the lightweight API's optional persistence integration. This ensures your stored embeddings and metadata remain available across sessions for continuous semantic search and RAG workflows.

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