• ISBN13: 9798291317402
• Binding: Paperback
• Subject: Computer Science and Information Technology
• Publisher: Independently Published
• Publisher Imprint: Independently Published
• Publication Date: July 6, 2025
• Pages: 168
• Original Price: GBP 16.12
• Language: English
• Edition: N/A
• Item Weight: 304 grams
• BISAC Subject(s): Data Science / Data Analytics

Vector Database Engineering is the ultimate guide to designing, building, and deploying scalable vector search systems using tools like FAISS, Milvus, Pinecone, Weaviate, and Qdrant. Whether you're building a semantic search engine, a personalized recommendation system, or an AI-powered chatbot, this book gives you the theoretical foundations, mathematical insights, and production-ready Python code you need to succeed.

What You'll Learn
Vector Embeddings & Similarity Search: Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances.
Vector Indexing at Scale: Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency.
Managed & Distributed Databases: Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant.
Real-World Applications: Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments.
Security & Compliance: Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion.
Advanced Topics: Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.

Key Use Cases
Semantic Search: Go beyond keywords using AI vector queries.
Recommendations: Suggest content and products based on behavior.
Multimedia Retrieval: Search images, audio, and video using embeddings.
RAG: Feed live vector data into LLMs for better answers.
Fraud & Anomaly Detection: Identify outliers with proximity-based search.
NLP & Generative AI: Embed, retrieve, and generate content with LLMs.

Why This Book?
Hands-On Python: 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate.
Math-Based Optimization: Understand latency, memory, and performance trade-offs.
Production Ready: Secure, scalable design patterns with best practices.
Future Trends: Includes neural retrievers, adaptive indexing, and multimodal workflows.

Who It's For

• Engineers building real-time search and recommendation engines

• ML and Data Scientists integrating vector search in pipelines

• DevOps deploying scalable and secure AI infrastructure

• AI researchers exploring retrieval-augmented generation

• Students and builders learning practical vector search

This is your in-depth, code-first guide to building intelligent, scalable vector database systems. Start using vector search to power the next generation of AI.

Get your copy now.