When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.The promise was intoxicating: Finally, a way to search by meaning rather than by brittle keywords. Just dump your enterprise knowledge into a vector store, connect an LLM and watch magic happen.Except the magic never fully materialized.Two years on, the reality check has arrived: 95% of organizations invested in gen AI initiatives are [...]
Vector databases emerged as a must-have technology foundation at the beginning of the modern gen AI era. What has changed over the last year, however, is that vectors, the numerical representations o [...]
Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehous [...]
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and [...]
A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate e [...]
Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has al [...]