Orama is a lightweight, in-browser and server-side search engine and RAG pipeline, designed for developers seeking to integrate full-text, vector, and hybrid search capabilities into their applications with minimal footprint. It supports advanced features like filtering, faceting, and geospatial search, enabling rich search experiences across various platforms.

How It Works

Orama utilizes an in-memory, column-oriented database architecture for high-performance search. It supports multiple search modes, including full-text (BM25, stemming, typo tolerance) and vector search, allowing for hybrid queries. The engine is highly extensible through a plugin system, enabling features like automatic embedding generation and secure API proxying for LLM integrations.

Quick Start & Requirements

• Installation: npm i @orama/orama
• Prerequisites: Node.js (for server-side/build-time usage), browser environment. TensorFlow.js is required for the @orama/plugin-embeddings plugin.
• Documentation: https://docs.orama.com/

Highlighted Details

• Full-text, vector, and hybrid search in under 2KB.
• Supports 10 data types, including vector[<size>].
• Plugin system for extending functionality (e.g., embedding generation, secure proxy).
• Enables RAG and chat experiences with LLM integrations.

Maintenance & Community

• Active development and community support via Slack.
• Roadmap and plugin development are ongoing.

Licensing & Compatibility

• Licensed under Apache 2.0.
• Permissive license suitable for commercial and closed-source applications.

Limitations & Caveats

The project is actively developed, and while core features are stable, advanced integrations like RAG and chat sessions may require careful configuration and understanding of external LLM APIs. Embedding generation requires specific backend configurations (e.g., @tensorflow/tfjs-node).