SKU/Artículo: AMZ-B0FK3X7Q53
Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.27 kg
0.27 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- 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 PineconeBuild and deploy LLM-integrated search pipelines using FastAPIMaster embedding generation with Hugging Face and OpenAIDesign scalable architectures for production-ready retrieval systemsHands-on examples with code that’s ready to adapt and extendStart developing the next generation of AI-powered applications. Grab your copy of "Vector Databases for Developers" today!
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