Orama Embeddable Search Engine Rag Pipeline

---
name: "Orama Embeddable Search Engine and RAG Pipeline for JavaScript"
slug: "orama-embeddable-search-engine-rag-pipeline"
description: "Orama is a full-text, vector, and hybrid search engine that runs in the browser, on a server, or at the edge in under 2KB. It provides built-in RAG pipeline support, typo tolerance, faceted search, and language-agnostic stemming — all without external dependencies."
github_stars: 10286
verification: "security_reviewed"
source: "https://github.com/oramasearch/orama"
category: "Data Extraction & Transformation"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "oramasearch/orama"
  github_stars: 10286
---

# Orama Embeddable Search Engine and RAG Pipeline for JavaScript

Orama is a full-text, vector, and hybrid search engine that runs in the browser, on a server, or at the edge in under 2KB. It provides built-in RAG pipeline support, typo tolerance, faceted search, and language-agnostic stemming — all without external dependencies.

## Installation

Use the upstream install or setup path that matches your environment:
- npm i @orama/orama
- With Deno, you can just use the same CDN URL or use npm specifiers:

Requirements and caveats from upstream:
- embedding: 'vector[1536]', // Vector size must be expressed during schema initialization
- import '@tensorflow/tfjs-node' // Or any other appropriate TensorflowJS backend, like @tensorflow/tfjs-backend-webgl

Basic usage or getting-started notes:
- You can install Orama using npm, yarn, pnpm, bun:
- sh
- Or import it directly in a browser module:

- Source: https://github.com/oramasearch/orama
- Extracted from upstream docs: https://raw.githubusercontent.com/oramasearch/orama/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/orama-embeddable-search-engine-rag-pipeline/)