Vector Database Engineering: Building Scalable AI Search & Retrieval Systems with FAISS, Milvus, Pinecone, Weaviate, RAG Pipelines, Embeddings, High D
暫譯: 向量資料庫工程：使用 FAISS、Milvus、Pinecone、Weaviate、RAG 管道、嵌入、高維度構建可擴展的 AI 搜索與檢索系統

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.