Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI

By Kenneth W. Moe

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

• Format: Paperback
• Language: English
• ISBN: B0FK3X7Q53
• ISBN13: 9798294333560
• Release Date: July 2025
• Publisher: Independently Published
• Length: 150 Pages
• Weight: 0.60 lbs.
• Dimensions: 0.3" x 7.0" x 10.0"