Weaviate helps developers of all levels build production-ready AI applications, in conjunction with the general availability of Gemini, Google’s new highly-capable multimodal AI model, Weaviate has released an integration that lets teams easily connect their own data to Gemini in a secure way.
As the amount of unstructured data within organizations grows, generative AI use cases like Retrieval Augmented Generation (RAG) are becoming increasingly popular. RAG techniques enhance Large Language Models (LLMs) with domain-specific or proprietary data to improve the accuracy and context of answers. The Weaviate module for Gemini enables the easy application of RAG with multimodal data—including text, images, and audio—while keeping data secure within a customer’s VPC. It’s as simple as connecting data to Weaviate, using a vectorization module to create embeddings, and enabling the Gemini module. Generating the best possible answers to user prompts has never been easier.
“The combination of sophistication, ease-of-use, and security in using Gemini with Weaviate is a powerful one,” said Bob van Luijt, CEO of Weaviate. “Enterprises can now unlock more value from their most complex data, without developers having to write a bunch of custom code.”
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Weaviate was built from the ground up to handle large-scale multimodal data for AI use cases. Its modular architecture includes native integration with popular AI models and frameworks to speed up development and allow flexibility as needs evolve. Customers using Weaviate and Gemini together can offer their users access to information that would have otherwise been unavailable, while protecting data security and developer experience. Gemini is a foundational building block and a powerful engine for driving future innovations in AI and Weaviate is excited to be part of it.
SOURCE: PRNewsWire