Teradata is enhancing its platform with a new Enterprise Vector Store that provides in-database vector support, primarily targeting applications necessitating retrieval augmented generation (RAG), such as agentic AI. This innovative offering includes features for comprehensive vector data management, from embedding generation and indexing to metadata management and intelligent search, streamlining the process of data utilization. Additionally, it supports the LangChain framework and introduces temporal vector embedding capabilities, which will assist in tracking data changes over time, thereby promoting explainability and improving decision-making accuracy.
Teradata's new Enterprise Vector Store introduces in-database vector support to enhance retrieval augmented generation (RAG) capabilities, particularly for agentic AI applications.
The Enterprise Vector Store includes comprehensive vector data management features, encompassing embedding generation, indexing, metadata management, and intelligent search for enhanced data utilization.
Supporting frameworks like LangChain, the Vector Store will also incorporate planned temporal vector embedding capabilities, which allow for tracking historical changes in data to improve accuracy.
Teradata emphasizes that the vector support not only augments data retrieval but also enhances explainability in AI, facilitating better decision-making through accurate data insights.
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