As AI developers strive to build faster, more accurate and contextually relevant Retrieval Augmented Generation (RAG) systems, they face significant challenges in efficiently managing large-scale unstructured data and delivering fast, accurate responses. To overcome these hurdles, SambaNova is working with Zilliz, a cloud-native software company, to showcase the power of combining fast inference with efficient vector databases. This collaboration provides developers with examples on how to build RAG solutions with faster inference, improved resource utilization, and real-time processing capabilities.
To make it easier for developers to start building applications, we have provided a few examples to accelerate development:
Two concise notebooks are available in the integrations folder, demonstrating how to implement RAG using Zilliz's Milvus and SambaNova's Llama models:
Both notebooks guide users through preparing embeddings, creating collections, and generating responses with context.
SambaNova Cloud and Milvus are pioneering platforms that are working better together and demonstrate the potential of combining cutting-edge vector database technology with faster inference. Developers can get started for free today by signing up for the SambaNova Cloud.