Orama is a search engine and vector database that provides full-text indexing, geospatial calculations, and semantic vector storage. It functions as an LLM retrieval engine designed to provide grounded context to language models for conversational interfaces.
The project implements hybrid search by combining dense vector embeddings with inverted keyword indices to retrieve documents based on both semantic meaning and exact text matches. It utilizes a WebAssembly module to execute search logic across different JavaScript environments and platforms.
The system covers a broad range of retrieval capabilities, including faceted search with category counts, geographical distance filtering, and typo tolerance. It also includes a middleware pipeline for integrating external plugins and tools for search result merchandising to influence document ranking via custom rules.
