Additional information
by Yudong Cai (Author), Jeremy Zhu (Author), Xuan Yang (Author)
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 Description:
The 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.
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What You Will Learn:
- Deploy Milvus using Docker, Kubernetes, and Helm
- Configure Milvus and monitor system health with Prometheus, Grafana, and Loki
- Understand core components like Knowhere, indexes, time sync, compaction, and garbage collection
- Design and optimize schema, queries, and data modification flows
- Benchmark performance and simulate real-world failure recovery
- Scale Milvus clusters to support large datasets and high-concurrency traffic
- Implement different multi-tenant strategies in Milvus
- Build AI applications using Milvus with LangChain
Who this book is for:
This book is for database practitioners looking to get started with Milvus and build their expertise in vector data and vector search. It's particularly suited for data analysts, data scientists, Milvus developers, system architects, tech enthusiasts, and researchers in vector database technologies.
To get the most out of this book, you should have a foundational understanding of Go, Python, or C++, as well as a basic knowledge of database systems. Familiarity with Docker and Kubernetes is recommended.
Table of Contents
- Introduction to Milvus
- Deploying Milvus in Multiple Ways
- Interacting with Milvus
- Configuring the Milvus System
- Understanding the Milvus Data Model and Architecture
- Data Modification and Maintenance in Milvus
- Reading Data in Milvus
- Compaction and Garbage Collection
- Exploring Milvus' Vector Engine
- How to Select a Vector Index
- Handling Complicated Search Requests
- Getting Started with Milvus Performance Benchmarking
(N.B. Please use the Read Sample option to see further chapters
