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Artículo: AMZ-183508950X
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
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Paperback
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0.71 kg
0.71 kg
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USA
Sobre este producto
- Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key FeaturesExamine text chunking effects on RAG workflows and understand security in RAG app developmentDiscover chatbots and agents and learn how to build complex conversation enginesBuild as you learn by applying the knowledge you gain to a hands-on projectBook DescriptionDiscover 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 learnUnderstand the LlamaIndex ecosystem and common use casesMaster techniques to ingest and parse data from various sources into LlamaIndexDiscover how to create optimized indexes tailored to your use casesUnderstand how to query LlamaIndex effectively and interpret responsesBuild an end-to-end interactive web application with LlamaIndex, Python, and StreamlitCustomize a LlamaIndex configuration based on your project needsPredict costs and deal with potential privacy issuesDeploy LlamaIndex applications that others can useWho this book is forThis 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.Table of ContentsUnderstanding Large Language ModelsLlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex EcosystemKickstarting Your Journey with LlamaIndexIngesting Data into Our RAG WorkflowIndexing with LlamaIndexQuerying Our Data, Part 1 – Context RetrievalQuerying Our Data, Part 2 – Postprocessing and Response SynthesisBuilding Chatbots and Agents with LlamaIndexCustomizing and Deploying Our LlamaIndex ProjectPrompt Engineering Guidelines and Best PracticesConclusions and Additional Resources
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