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Building Data-Driven applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) enhance LLM
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Building Data-Driven applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) enhance LLM in Franklin, TN
Current price: $44.99
Barnes and Noble
Building Data-Driven applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) enhance LLM in Franklin, TN
Current price: $44.99
Loading Inventory...
Size: Paperback
Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications
Key Features
Examine text chunking effects on RAG workflows and understand security in RAG app development
Discover chatbots and agents and learn how to build complex conversation engines
Build as you learn by applying the knowledge you gain to a hands-on project
Book Description
Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learn
Understand the LlamaIndex ecosystem and common use cases
Master techniques to ingest and parse data from various sources into LlamaIndex
Discover how to create optimized indexes tailored to your use cases
Understand how to query LlamaIndex effectively and interpret responses
Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
Customize a LlamaIndex configuration based on your project needs
Predict costs and deal with potential privacy issues
Deploy LlamaIndex applications that others can use
Who this book is for
This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.
Key Features
Examine text chunking effects on RAG workflows and understand security in RAG app development
Discover chatbots and agents and learn how to build complex conversation engines
Build as you learn by applying the knowledge you gain to a hands-on project
Book Description
Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learn
Understand the LlamaIndex ecosystem and common use cases
Master techniques to ingest and parse data from various sources into LlamaIndex
Discover how to create optimized indexes tailored to your use cases
Understand how to query LlamaIndex effectively and interpret responses
Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
Customize a LlamaIndex configuration based on your project needs
Predict costs and deal with potential privacy issues
Deploy LlamaIndex applications that others can use
Who this book is for
This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.
Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications
Key Features
Examine text chunking effects on RAG workflows and understand security in RAG app development
Discover chatbots and agents and learn how to build complex conversation engines
Build as you learn by applying the knowledge you gain to a hands-on project
Book Description
Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learn
Understand the LlamaIndex ecosystem and common use cases
Master techniques to ingest and parse data from various sources into LlamaIndex
Discover how to create optimized indexes tailored to your use cases
Understand how to query LlamaIndex effectively and interpret responses
Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
Customize a LlamaIndex configuration based on your project needs
Predict costs and deal with potential privacy issues
Deploy LlamaIndex applications that others can use
Who this book is for
This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.
Key Features
Examine text chunking effects on RAG workflows and understand security in RAG app development
Discover chatbots and agents and learn how to build complex conversation engines
Build as you learn by applying the knowledge you gain to a hands-on project
Book Description
Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learn
Understand the LlamaIndex ecosystem and common use cases
Master techniques to ingest and parse data from various sources into LlamaIndex
Discover how to create optimized indexes tailored to your use cases
Understand how to query LlamaIndex effectively and interpret responses
Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
Customize a LlamaIndex configuration based on your project needs
Predict costs and deal with potential privacy issues
Deploy LlamaIndex applications that others can use
Who this book is for
This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.
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