Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI - Softcover
Inhaltsangabe
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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Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
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Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
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Vector Databases for Developers (Paperback)
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Paperback. Zustand: new. Paperback. Unlock the Power of Vectors in AI ApplicationsDiscover 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! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798294333560
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Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
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Vector Databases for Developers: Hands-On Implementation of Embedding-Based Search Engines and LLM Retrieval with Python and FastAPI
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Vector Databases for Developers (Paperback)
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Paperback. Zustand: new. Paperback. Unlock the Power of Vectors in AI ApplicationsDiscover 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! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798294333560
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