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Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
Paperback
Publisher: Independently Published
Published: Jul 26 2025
Pages: 150
Weight: 0.60
Height: 0.32 Width: 7.00 Depth: 10.00
Language: English
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!
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