Overview

Unlock the Power of Vectors in AI Applications Discover how modern developers are building intelligent search and retrieval systems with embeddings, vector databases, and Python-powered APIs. Vector databases are at the heart of AI-native applications from semantic search to RAG-powered LLM systems. This hands-on guide empowers developers to build real-world, production-ready vector search engines using Python, FastAPI, and open-source tools. Inside, you'll learn how to generate embeddings, store them efficiently, and build scalable retrieval systems using top-tier vector databases like FAISS, Qdrant, Milvus, and Pinecone. Through structured chapters and practical code examples, the book walks you through indexing strategies, similarity search, LLM integration, and full-stack deployment all from a developer's perspective. Whether you're developing custom search engines, recommendation systems, or AI chatbots, this book offers the practical foundation and tools you need to confidently implement vector-based solutions in your software projects. Key Features: Step-by-step tutorials on FAISS, Qdrant, Weaviate, Milvus, and Pinecone Build and deploy LLM-integrated search pipelines using FastAPI Master embedding generation with Hugging Face and OpenAI Design scalable architectures for production-ready retrieval systems Hands-on examples with code that's ready to adapt and extend Start developing the next generation of AI-powered applications. Grab your copy of ""Vector Databases for Developers"" today!

Full Product Details

9798294333560

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available