Algolia AI Search

AI search and vector database platform that provides unified vector search with semantic understanding, hybrid search capabilities, and developer-friendly APIs for building intelligent search applications.

Vector search capabilities in Apache Solr with HNSW indexing, early termination optimization, and integrated text-to-vector capabilities for hybrid search applications.

A search approach combining traditional keyword-based BM25 ranking with modern vector similarity search. By leveraging both lexical matching and semantic understanding, hybrid search provides superior retrieval quality through techniques like reciprocal rank fusion (RRF) to merge results from both methods.

OpenSearch Vector Search is the vector similarity search and AI search capability within the OpenSearch engine, supporting vector indices, ingestion of embedding data, and search methods including raw vector search, semantic search, hybrid search, multimodal search, and neural sparse search. It enables building RAG and conversational search applications using either user-provided embeddings or embeddings generated automatically by OpenSearch.

Neural and hybrid search capability in OpenSearch that combines lexical queries with vector-based neural search using a pipeline of normalization and score combination techniques. It enables semantic (vector) search and hybrid search over indices such as `neural_search_pqa`, suitable for AI and vector database-style retrieval use cases.

Meilisearch offers vector search capabilities as part of its search engine, enabling hybrid and semantic search for AI applications.