• ISBN13: 9798268983814
• Binding: Paperback
• Subject: Computer Science and Information Technology
• Publisher: Independently Published
• Publisher Imprint: Independently Published
• Publication Date: October 8, 2025
• Pages: 238
• Original Price: GBP 12.66
• Language: English
• Edition: N/A
• Item Weight: 418 grams
• BISAC Subject(s): Data Science / Data Modeling & Design

"Vector Database Design and Implementation" is the definitive guide to creating robust, scalable vector search systems with FAISS, Milvus, Pinecone, Weaviate, and LangChain. Perfect for building semantic search, recommendation engines, or RAG-based chatbots, this book delivers practical Python code, theoretical insights, and production-ready strategies to master AI data infrastructure.

What You'll Learn

• Vector Embeddings & Search: Transform text, images, and data into vectors; master similarity search with cosine, Euclidean, and dot product metrics.
• Scalable Indexing: Implement HNSW, IVF, and PQ with FAISS; optimize Milvus and Weaviate for high recall and low latency.
• Managed vs. Self-Hosted: Leverage Pinecone's serverless ease or deploy Milvus/Weaviate on Kubernetes for control.
• Real-World Applications: Build enterprise search, e-commerce recommendations, multimodal retrieval, and RAG pipelines with LangChain.
• Security & Compliance: Secure data with AES-256, TLS, RBAC, and GDPR-compliant anonymization.
• Advanced Techniques: Explore hybrid search, CLIP-based multimodal embeddings, and emerging trends like adaptive indexing.

Key Use Cases

• Semantic Search: Power intelligent queries beyond traditional keywords.
• Recommendations: Deliver personalized product or content suggestions.
• Multimedia Retrieval: Search images or videos using text queries.
• RAG Pipelines: Enhance LLMs with dynamic, context-rich data retrieval.
• Anomaly Detection: Identify outliers using vector proximity.
• Generative AI: Integrate embeddings with LLMs for smarter outputs.

Why This Book?

• Hands-On Code: 50+ Python examples using FAISS, Milvus, Pinecone, Weaviate, and LangChain.
• Performance Focus: Optimize latency, throughput, and recall with mathematical rigor.
• Production Ready: Deploy secure, scalable systems with CI/CD and monitoring.
• Future-Proof: Covers neural search, federated learning, and edge deployments.

Who It's For

• Engineers crafting real-time AI search and recommendation systems.
• Data scientists integrating vector search into ML pipelines.
• DevOps professionals deploying secure, scalable AI infrastructure.
• AI researchers advancing RAG and multimodal applications.
• Students and enthusiasts mastering vector database technologies.

This comprehensive, code-driven guide empowers you to build intelligent, high-performance vector database systems for next-generation AI applications. Get your copy today and start transforming data into actionable intelligence.