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Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback 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. Preface Who this book is for What this book covers To get the most out of this book Get in touch Why Retrieval Augmented Generation? What is RAG? Naïve, advanced, and modular RAG configurations RAG versus fine-tuning The RAG ecosystem The retriever (D) Collect (D1) Process (D2) Storage (D3) Retrieval query (D4) The generator (G) Input (G1) Augmented input with HF (G2) Prompt engineering (G3) Generation and output (G4) The evaluator (E) Metrics (E1) Human feedback (E2) The trainer (T) Naïve, advanced, and modular RAG in code 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 1. Retriever 1.1. Installing the retriever’s environment 1.2.1. Preparing the dataset 1.2.2. Processing the data 1.3. Retrieval process for user input 2. Generator 2.1. Integrating HF-RAG for augmented document inputs 2.2. Input 2.3. Mean ranking simulation scenario 2.4.–2.5. Installing the generative AI environment 2.6. Content generation 3. Evaluator 3.1. Response time 3.2. Cosine similarity score 3.3. Human user rating 3.4. Human-expert evaluation Summary Questions References Further reading Scaling RAG Bank Customer Data with Pinecone Scaling with Pinecone Architecture Pipeline 1: Collecting and preparing the dataset 1. Collecting and processing the dataset Installing the environment for Kaggle Collecting the dataset 2. Exploratory data analysis 3. Training an ML model Data preparation and clustering Implementation and evaluation of clustering Pipeline 2: Scaling a Pinecone index (vector store) The challenges of vector store management Installing the environment Processing the dataset Chunking and embedding the dataset Chunking Embedding Duplicating data Creating the Pinecone index Upserting Querying the Pinecone index Pipeline 3: RAG generative AI RAG with GPT-4o Querying the dataset Querying a target vector Extracting relevant texts Augmented prompt Augmented generation Summary Questions References Further reading Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex The architecture of RAG for knowledge-graph-based semantic search Building graphs from trees Pipeline 1: Collecting and preparing the documents Retrieving Wikipedia data and metadata Preparing the data for upsertion Pipeline 2: Creating and populating the Deep Lake vector store Pipeline 3: Knowledge graph index-based RAG Generating the knowledge graph index Displaying the graph Interacting with the knowledge graph index Installing the similarity score packages and defining the functions Re-ranking Example metrics Metric calculation and display Summary Questions References Further reading Dynamic RAG with Chroma and Hugging Face Llama The architecture of dynamic RAG Installing the environment Hugging Face Chroma Activating session time Downloading and preparing the dataset Embedding and upserting the data in a Chroma collection Selecting a model Embedding and storing the completions Displaying the embeddings Querying the collection Prompt and retrieval RAG with Llama Deleting the collection Total session time Summary Questions References Further reading Empowering AI Models: Fine-Tuning RAG Data and Human Feedback The architecture of fine-tuning static RAG data The RAG ecosystem Installing the environment 1. Preparing the dataset for fine-tuning 1.1. Downloading and visualizing the dataset 1.2. Preparing the dataset for fine-tuning 2. Fine-tuning the model 2.1. Monitoring the fine-tunes 3. Using the fine-tuned OpenAI model Metrics Summary Questions References Further reading RAG for Video Stock Production with Pinecone and OpenAI The architecture of RAG for video production The environment of the video production ecosystem Importing modules and libraries GitHub OpenAI Pinecone Pipeline 1: Generator and Commentator The AI-generated video dataset How does a diffusion transformer work? Analyzing the diffusion transformer model video dataset The Generator and the Commentator Step 1. Displaying the video Step 2. Splitting video into frames Step 3. Commenting on the frames Pipeline 1 controller Pipeline 2: The Vector Store Administrator Querying the Pinecone index Pipeline 3: The Video Expert Summary Questions References Further reading Appendix Index Explore the transformative potential of RAG-driven LLMs, computer vision, and generative AI with this comprehensive guide, from basics to building a complex RAG pipeline