RAG Driven Generative AI 1st Edition by Denis Rothman ISBN 1836200919 9781836200918 by Denis Rothman instant download after payment.

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Product details:

ISBN 10: 1836200919 
ISBN 13: 9781836200918
Author: Denis Rothman

Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free

Key Features
Implement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agents
Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs
Balance cost and performance between dynamic retrieval datasets and fine-tuning static data
Book Description
RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.
What you will learn
Scale RAG pipelines to handle large datasets efficiently
Employ techniques that minimize hallucinations and ensure accurate responses
Implement indexing techniques to improve AI accuracy with traceable and transparent outputs
Customize and scale RAG-driven generative AI systems across domains
Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval
Control and build robust generative AI systems grounded in real-world data
Combine text and image data for richer, more informative AI responses
Who this book is for
This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you’ll find this book useful.

RAG Driven Generative AI 1st Table of contents:

Part 1: Foundations and basic implementation
1. Environment
2. The generator
3. The Data
4.The query
Part 2: Advanced techniques and evaluation
1. Retrieval metrics
2. Naïve RAG
3. Advanced RAG
4. Modular RAG
Summary
Questions
References
Further reading
RAG Embedding Vector Stores with Deep Lake and OpenAI
From raw data to embeddings in vector stores
Organizing RAG in a pipeline
A RAG-driven generative AI pipeline
Building a RAG pipeline
Setting up the environment
The installation packages and libraries
The components involved in the installation process
1. Data collection and preparation
Collecting the data
Preparing the data
2. Data embedding and storage
Retrieving a batch of prepared documents
Verifying if the vector store exists and creating it if not
The embedding function
Adding data to the vector store
Vector store information
3. Augmented input generation
Input and query retrieval
Augmented input
Evaluating the output with cosine similarity
Summary
Questions
References
Further reading
Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI
Why use index-based RAG?
Architecture
Building a semantic search engine and generative agent for drone technology
Installing the environment
Pipeline 1: Collecting and preparing the documents
Pipeline 2: Creating and populating a Deep Lake vector store
Pipeline 3: Index-based RAG
User input and query parameters
Cosine similarity metric
Vector store index query engine
Query response and source
Optimized chunking
Performance metric
Tree index query engine
Performance metric
List index query engine
Performance metric
Keyword index query engine
Performance metric
Summary
Questions
References
Further reading
Multimodal Modular RAG for Drone Technology
What is multimodal modular RAG?
Building a multimodal modular RAG program for drone technology
Loading the LLM dataset
Initializing the LLM query engine
Loading and visualizing the multimodal dataset
Navigating the multimodal dataset structure
Selecting and displaying an image
Adding bounding boxes and saving the image
Building a multimodal query engine
Creating a vector index and query engine
Running a query on the VisDrone multimodal dataset
Processing the response
Selecting and processing the image of the source node
Multimodal modular summary
Performance metric
LLM performance metric
Multimodal performance metric
Multimodal modular RAG performance metric
Summary
Questions
References
Further reading
Boosting RAG Performance with Expert Human Feedback
Adaptive RAG
Building hybrid adaptive RAG in Python

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Tags: Denis Rothman, RAG, Generative