Author Description

Co-authored by core contributors of Milvus, this book guide explores the architecture of the Milvus vector databases for GenAI solutions Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Understand the core architecture and vector indexing engine that makes Milvus ideal for AI-driven search Learn scalable deployment and performance optimization techniques Test, apply, and integrate Milvus into AI and LLM pipelines using LangChain Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe rapid adoption of LLMs demands efficient storage and lightning-fast retrieval of unstructured data. Designed as a vector database, Milvus has earned widespread recognition in the community and support from tech giants like Apple and NVIDIA. Yet, many developers only scratch the surface of what Milvus is truly capable of. Written by the contributors of the Milvus project, this handbook gives you an insider’s perspective on its design and how it handles large-scale, high-dimensional vector data. Starting with the basics, you’ll learn about everything from service deployment and SDK usage to Milvus’ layered architecture and how its components interact. You’ll learn how the indexing, replication, compaction, and garbage collection systems work and how to apply them to real scenarios. Through practical demos and configuration exercises, you’ll learn how to monitor, scale, and secure Milvus in production and then advance to performance evaluation and scalability testing using tools like VectorDBBench. You'll also explore Milvus' integration with LangChain for use cases such as vector search and RAG-based chatbots. By the end of this book, you’ll be able to analyze Milvus internals, fine-tune for performance, ensure system stability, and integrate it into next-generation AI solutions. *Email sign-up and proof of purchase requiredWhat you will learn D