Leveraging Milvus and Friendli Serverless Endpoints for Advanced RAG and Multi-Modal Queries

FriendliAI specializes in generative AI infrastructure, offering solutions that enable organizations to efficiently deploy and manage large language models (LLMs) and other generative AI models. Milvus is an open-source vector database that stores, indexes, and searches billion-scale unstructured data through high-dimensional vector embeddings. It's perfect for building modern AI applications such as retrieval augmented generation (RAG), semantic search, multimodal search, and recommendation systems. The combination of RAG and multi-modal models significantly improves AI systems by providing diverse and rich input types, up-to-date information, enhanced accuracy and relevance of responses, context-aware interactions, allowing for more accurate and nuanced interactions. By leveraging Milvus and Friendli Serverless Endpoints, users can perform Retrieval-Augmented Generation (RAG) on particular documents and materials and execute multi-modal queries that incorporate images and other visual content. The tutorial demonstrates how to use Milvus with Friendli Serverless Endpoints to perform RAG on specific documents and materials and execute multi-modal queries that include images. It also showcases the combination of RAG and multi-modal capabilities, enabling more sophisticated AI applications that can understand and process diverse types of information, leading to more accurate and context-aware responses.