The Future of Vector Search

Since we released a vector database index nearly two years ago, the landscape of vector search and databases has evolved dramatically. The rise of Retrieval-Augmented Generation (RAG) has been a pivotal factor, with embeddings emerging as the lingua franca of Generative AI. This paradigm shift has spurred a surge in new systems, with the emergence of numerous vector search and database startups. Additionally, established data management platforms like Postgres, Databricks, MongoDB, and Neo4j have integrated vector search capabilities into their offerings.

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