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Vector Database Engineering: Building Scalable AI Search & Retrieval Systems with FAISS, Milvus, Pinecone, Weaviate, and LangChain Agents (with ... ...
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 Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances.
Vector Indexing at Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency.
Managed & Distributed Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant.
Real-World Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments.
Security & Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion.
Advanced Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.
Key Use Cases
Semantic Go beyond keywords using AI vector queries.
Suggest content and products based on behavior.
Multimedia Search images, audio, and video using embeddings.
Feed live vector data into LLMs for better answers.
Fraud & Anomaly Identify outliers with proximity-based search.
NLP & Generative Embed, retrieve, and generate content with LLMs.
Why This Book?
Hands-On 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate.
Math-Based Understand latency, memory, and performance trade-offs.
Production Secure, scalable design patterns with best practices.
Future 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.
What You’ll Learn
Vector Embeddings & Similarity Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances.
Vector Indexing at Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency.
Managed & Distributed Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant.
Real-World Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments.
Security & Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion.
Advanced Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.
Key Use Cases
Semantic Go beyond keywords using AI vector queries.
Suggest content and products based on behavior.
Multimedia Search images, audio, and video using embeddings.
Feed live vector data into LLMs for better answers.
Fraud & Anomaly Identify outliers with proximity-based search.
NLP & Generative Embed, retrieve, and generate content with LLMs.
Why This Book?
Hands-On 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate.
Math-Based Understand latency, memory, and performance trade-offs.
Production Secure, scalable design patterns with best practices.
Future 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.
169 pages, Kindle Edition
Published July 5, 2025
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