The Rise of Vector and Graph Databases in Generative AI Implementations
This IDC Perspective explores the development of multimodal capabilities through vector databases in generative AI (GenAI). It delves into constructing a GenAI ecosystem, ranging from establishing the data layer to highlighting the significance of vector and graph databases for GenAI development."As organizations discover value from GenAI, there is a move toward building bespoke solutions using these large language models (LLMs). Vector databases provide highly efficient storage mechanisms for multimodal enterprise data and augment search and recommendation capabilities from huge data sets. Vector databases are now an integral part of the GenAI data value chain," says Deepika Giri, associate VP, Data and AI, IDC Asia/Pacific.
Please Note: Extended description available upon request.
- Executive Snapshot
- Situation Overview
- Building a Robust Generative AI Ecosystem
- Expanding the Data Layer for GenAI: The Need for Multimodal Capabilities
- Build Versus Buy
- The Rise of Vector Databases
- Retrieval-Augmented Generation
- RAG Architectures Explained
- RAG Paradigms
- Advanced RAG
- Graph Databases for GenAI
- Advice for the Technology Buyer
- Learn More
- Related Research
- Synopsis
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