Vector Databases and RAG with Python: Build intelligent search and retrieval systems using embeddings and LLMs (English Edition)

As large language models continue to transform how we build intelligent systems, the ability to integrate proprietary data through vector search and RAG has become essential for creating accurate, contextually-aware applications that go beyond the limitations of pre-trained models

This comprehensive guide takes you from foundational concepts to production-ready implementations of vector databases and RAG systems. Starting with vector semantics and embeddings, you will learn to generate vector representations using neural networks, BERT, and OpenAI models. The book covers popular vector databases including Weaviate and Milvus, teaching you how to implement efficient search algorithms like k-nearest neighbors and hierarchical navigable small worlds. You will build complete RAG pipelines, explore advanced techniques like GraphRAG, and master evaluation frameworks using LlamaIndex. Each chapter includes hands-on Python examples with practical code implementations that demonstrate real-world applications.

By the end of this book, you will have mastered the skills needed to design, build, and evaluate production-grade vector search systems and RAG applications. You will be equipped to enhance LLM applications with private data, implement semantic search at scale, troubleshoot retrieval issues, and solve real-world information retrieval challenges using cutting-edge AI techniques with confidence.

What you will learn

● Generate embeddings using neural networks, BERT, and OpenAI models.

● Implement vector search algorithms including KNN and HNSW.

● Develop GraphRAG systems for structured knowledge representation.

● Evaluate and optimize RAG applications using LlamaIndex frameworks.

● Design scalable vector database architectures for production environments.

● Integrate vector search with LLMs for intelligent retrieval.

Who this book is for

This book is designed for data scientists, machine learning engineers, and software developers who want to build intelligent search and retrieval systems using modern AI techniques. It is ideal for professionals working with large language models who need to integrate private data, implement semantic search capabilities, or build production-ready RAG applications.

Table of Contents

1. Introduction to Vector Search

2. Getting Vector Representation

3. Searching using Vectors

4. Nearest Neighbor Search

5. Vector Databases Weaviate

6. Vector Databases Milvus

7. Solving RAG Use Cases with Milvus and Weaviate

8. Graph RAG

9. RAG Introduction with LlamaIndex

10. Evaluating RAG

Rajdeep Dua is a seasoned engineering leader with over 25 years of experience spanning software development at the intersection of enterprise systems, artificial intelligence, and cloud computing. He has led engineering teams at Salesforce, VMware, Google, and Microsoft, and currently builds AI applications for industry verticals using predictive and generative machine learning and deep learning models. He has authored more than five books on machine learning and containers, filed more than 30 patents, and received over 1,100 citations for his papers, books, and patents. He is passionate about how generative AI and contextual knowledge help solve use cases that cannot be addressed by out-of-the-box large language models alone.