Orama is a high-performance, embeddable search engine designed for developers who need full-text, vector, and hybrid search capabilities without the overhead of external services. It’s ideal for frontend applications, static sites, edge functions, and serverless environments where low latency and small bundle size are critical. With native support for BM25, stemming in 30 languages, geosearch, and AI-powered answer engines, Orama eliminates the need for complex search infrastructure.
Built in TypeScript and optimized for modern JavaScript runtimes, Orama runs on Node.js, Deno, browsers via CDN, and edge platforms. It supports plugins for embeddings, secure OpenAI proxying, data persistence, and analytics, and integrates seamlessly with frameworks like Vitepress, Docusaurus, and Astro. Deployment is as simple as importing a single ES module.
What You Get
- Full-Text Search - Supports BM25 ranking, stemming in 30 languages, typo tolerance, exact match, and field boosting for precise text relevance.
- Vector Search - Native vector search with configurable vector dimensions (e.g., vector[1536]), similarity thresholds, and optional embedding inclusion in results.
- Hybrid Search - Combines full-text and vector search results using configurable weighting for improved relevance in RAG and semantic search applications.
- Geosearch - Filter and rank results by geographic coordinates using geopoint data type with lat/lon support.
- GenAI Chat Sessions - Build chatbot-like experiences using AnswerSession with OpenAI models via Secure Proxy plugin, powered by retrieved documents and custom system prompts.
- Search Filters & Facets - Apply filters on string, number, boolean, and enum fields; generate dynamic facets for UI navigation and drill-downs.
- Pinning Rules (Merchandising) - Force specific documents to appear at the top of search results for promotional or editorial control.
- Plugin System - Extend functionality with official plugins for embeddings, secure OpenAI proxying, data persistence, analytics, and framework integrations (Vitepress, Docusaurus, Astro).
- Text Analysis & Tokenization - Automatic stemming, tokenization, and language-aware processing for 30+ languages including Chinese and Japanese.
- Embedding Generation Plugin - Automatically generate text embeddings at insert time using TensorFlow.js or OpenAI via plugin-embeddings, eliminating manual embedding management.
- Secure Proxy Plugin - Call OpenAI APIs securely from the browser or edge without exposing API keys, enabling client-side RAG without backend dependencies.
- Multi-Platform Support - Runs in browsers (via CDN), Node.js, Deno, and edge runtimes with identical APIs and zero external dependencies.
Common Use Cases
- Building a client-side product catalog search - An e-commerce frontend uses Orama to power instant search with filters, geosearch, and typo tolerance without calling a backend API.
- Adding RAG-powered help chat to a documentation site - A SaaS company embeds Orama with plugin-embeddings and plugin-secure-proxy to let users ask questions about docs using GPT-4o-mini without exposing API keys.
- Deploying a search engine on edge functions - A developer uses Orama in a Cloudflare Worker to serve low-latency search results for a static blog with 10k+ articles and vector embeddings.
- Creating a localized search experience for multilingual content - A global news platform uses Orama’s 30-language stemming and tokenization to enable accurate search in Japanese, Chinese, and French without external services.
Under The Hood
Architecture
- Monorepo structure using pnpm and Turbo enforces clear package boundaries, with a core search engine module decoupled from plugins and utilities via explicit dependencies
- Modular design isolates stemmers, tokenizers, and stopwords as reusable components, promoting separation of concerns and independent testing
- Dependency inversion is achieved through TypeScript interfaces and workspace links, enabling plugin extensibility without heavyweight injection frameworks
- Build pipeline leverages Turbo’s task graph to enforce correct build order and optimize caching across multiple packages
Tech Stack
- TypeScript-first codebase with modern tooling including SWC for fast compilation, Biome for linting, and TSX for development workflows
- Comprehensive testing suite with Tap, Tape, and Playwright covering unit, integration, and end-to-end scenarios, supported by code coverage and pre-commit hooks
- Browser and edge-optimized engine built with minimal dependencies, utilizing WebAssembly and native JavaScript for full-text, vector, and hybrid search
- Automated deployment pipelines with semantic release, changelog generation, and commit-based versioning streamline releases across environments
Code Quality
- Extensive test coverage ensures correctness across search, filtering, and vector operations, including edge cases like nested properties and type validation
- Strong type safety through strict schemas and const assertions prevents runtime errors in complex data structures and embeddings
- Explicit, context-aware error handling improves developer experience by surfacing precise issues in schema definitions and vector inputs
- Consistent naming, clean API design, and minimalistic tooling prioritize clarity, reliability, and maintainability over complexity
What Makes It Unique
- Hybrid search algorithm dynamically balances keyword overlap and threshold-based filtering to control exact vs. fuzzy match behavior without external dependencies
- Internal document ID mapping system efficiently translates string identifiers to compact integers, optimizing memory and serialization for large-scale indexing
- Pure JavaScript stemmer implements rule-based morphological reduction, eliminating reliance on external NLP libraries while maintaining high accuracy
- Self-contained, framework-agnostic search engine integrates seamlessly with frontend frameworks like Next.js and Docusaurus, removing the need for backend search services
- End-to-end search functionality built from first principles, offering a lightweight, embeddable alternative to traditional search engines like Elasticsearch