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Next-Gen Vector Databases: Hands-On Techniques for High-Dimensional Search, Multimodal Retrieval, and AI-Powered Applications.
Paperback
Series: Vector Database Mastery: From Foundations to Production-Ready AI Search Systems, Book 2
Publisher: Independently Published
Published: Dec 8 2025
Pages: 136
Weight: 0.55
Height: 0.29 Width: 7.00 Depth: 10.00
Language: English
- High-Dimensional Vector Spaces: Master the mathematical foundations of embeddings, distance metrics, and dimensionality reduction.
- Adaptive and Distributed Indexing: Implement HNSW, IVF, PQ, and hybrid indices for real-time, large-scale search.
- Multimodal Retrieval: Integrate text, images, audio, and video into unified vector spaces for AI-powered search.
- Neural and Retrieval-Augmented Generation (RAG): Combine vector search with LLMs to build next-level chatbots, recommendation engines, and knowledge systems.
- Edge and Federated Search: Deploy AI search pipelines across distributed environments with privacy-preserving embeddings.
- Performance, Security, and Optimization: Scale, accelerate, and secure your vector database infrastructure for production workloads.
Why This Book?
- Advanced, code-first guidance for modern vector search systems
- Production-ready design patterns with security and compliance best practices
- Deep dive into neural retrieval, adaptive indexing, and multimodal pipelines
- Real-world use cases across search, recommendation, AI, and generative applications
- AI and ML engineers building large-scale search and recommendation systems
- Data scientists integrating vector retrieval into analytics and pipelines
- DevOps professionals deploying distributed, high-performance vector databases
- Researchers exploring retrieval-augmented generation, multimodal search, and next-gen AI applications
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