Module 2 Introduction - Advanced Retrieval Augmented Generation

The "Advanced Retrieval Augmented Generation" module offers an in-depth exploration into optimizing large language models (LLMs) with advanced Retrieval-Augmented Generation (RAG) techniques. Across four lessons, it encompasses a range of topics including query transformation, re-ranking, optimization techniques like fine-tuning, the implementation of Activeloop's Deep Memory, and other advanced strategies using LlamaIndex. Students will gain practical experience in enhancing RAG system performance, from query refinement to production deployment and iterative optimization. The module is designed to provide a comprehensive understanding of building, refining, and deploying efficient RAG systems, integrating hands-on examples with theoretical knowledge to prepare students for real-world applications.

Fine-tuning vs RAG; Introduction to Activeloop’s Deep Memory;

In this lesson, students will explore various optimization techniques to maximize the performance of large language models (LLMs), such as prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). The lesson begins with identifying the benefits and challenges of each method. It further examines the limitations of RAG systems and introduces Activeloop's Deep Memory as a solution to these challenges, particularly in improving retrieval precision for user queries. Students will see a step-by-step guide on how to implement Deep Memory in experimental workflows, including creating a synthetic training dataset and running inference with the trained Deep Memory model. A significant portion of the lesson is dedicated to hands-on examples using code to demonstrate the increased recall rates when Deep Memory is applied in a RAG system. The lesson concludes with a comparison of empirical data, highlighting the advantages of Deep Memory over traditional methods and emphasizing its role in advancing the capabilities of LLMs.

Mastering Advanced RAG Techniques with LlamaIndex

In this lesson, students will learn about the advanced techniques and strategies that enhance the performance of Retrieval-Augmented Generation (RAG) systems, using LlamaIndex as a framework. They will explore the concepts of query construction, query expansion, and query transformation to refine the information retrieval process. Students will also be introduced to advanced strategies like reranking with Cohere Reranker, recursive retrieval, and small-to-big retrieval to further improve the quality and relevance of search results. The lesson includes hands-on examples of setting up a query engine from indexing to querying, as well as creating custom retrievers and utilizing reranking. The conclusion underlines the importance of these techniques and strategies in developing more efficient RAG-based applications.

Production-Ready RAG Solutions with LlamaIndex

In this lesson, students will learn about the challenges, optimization strategies, and best practices for Retrieval-Augmented Generation (RAG) systems in production. The discussion includes dealing with dynamic data management, diverse representation in latent space, regulatory compliance, and model selections for system efficiency. The lesson emphasizes the importance of fine-tuning both the embedding models and the Language Large Models (LLMs) to improve retrieval metrics and generate more accurate responses. Additionally, students will explore the role of Intel® technologies in optimizing neural network models on CPUs, and they will acquire knowledge on utilizing generative feedback loops, hybrid searches, and the continuous evaluation of RAG system performance. Practical use cases, data management tools, and integration of metadata in retrieval steps are also highlighted, with LlamaIndex being presented as a comprehensive framework for building data-driven LLM applications.

Iterative Optimization of LlamaIndex RAG Pipeline: A Step-by-Step Approach

In this lesson, you will learn the process of iteratively optimizing a LlamaIndex Retrieval-Augmented Generation (RAG) pipeline to enhance its performance in information retrieval and generating relevant answers. The lesson guides you through establishing a baseline pipeline, experimenting with retrieval values and embedding models like "text-embedding-ada-002" and "cohere/embed-english-v3.0," and incorporating techniques like reranking and deep memory to refine document selection. Additionally, you will learn about performance metrics, such as Hit Rate and Mean Reciprocal Rank (MRR), and evaluate faithfulness and relevancy of answers using GPT-4 as a judge. The lesson provides hands-on code examples for each optimization step and concludes with the overall enhancement observed in the RAG pipeline's accuracy.

Use Deep Memory to Boost RAG Apps' Accuracy by up to +22%

In this lesson, you will be introduced to a practical example of Deep Memory. Students will learn about the limitations of current RAG systems, such as suboptimal retrieval accuracy, and explore the benefits of implementing Deep Memory. The lesson explains how Deep Memory provides a significant accuracy boost by optimizing the vector search process using a tailored index from labeled queries. Throughout the lesson, students will be guided through hands-on examples for adopting Deep Memory in their applications, including data loading, creating a relevance dataset, training, and evaluating methods. The lesson emphasizes the practical advantages of this approach, like higher retrieval quality, cost savings from reduced context size needs and compatibility with existing workflows.

How to Use Deep Memory with LlamaIndex to Get +15% RAG hit_rate Improvement for Question Answering on Docs?

In this comprehensive tutorial, students will learn about improving the hit rate of Retrieval-Augmented Generators (RAGs) when answering questions from documentation by up to 15% or more using Activeloop's Deep Memory. The lesson covers dataset creation and ingestion using BeautifulSoup and LlamaIndex, training deep memory with synthetic queries, evaluating the performance improvement, and leveraging deep memory for real-world inference. By integrating a small neural network layer into the retrieval process, the tutorial demonstrates how to precisely match user queries with relevant data, significantly boosting the accuracy of returned information while maintaining minimal search latency. Students will get hands-on experience with Python libraries and AI models such as OpenAI's GPT-4 and vector store operations to create a more efficient and accurate RAG system.

Use Deep Memory with LangChain to Get Up to +22% Increase in Accurate Questions Answers to LangChain Code DB

In this lesson, students will learn how to utilize Activeloop Deep Memory with Langchain to enhance the efficiency and accuracy of Retrieval-Augmented Generation (RAG) systems by parsing documentation, creating datasets, generating synthetic queries, training a retrieval model, evaluating performance, and ultimately integrating Deep Memory into RAG-powered Language Learning Model (LLM) applications. They'll be guided through the practical steps involved in setting up this system, including library installation, data scraping and transformation, model training and evaluation, and even cost-saving measures, all while focusing on the balance between recall, cost, and latency in AI retrieval tasks.

Deep Memory for RAG Applications Across Legal, Financial, and Biomedical Industries

In this comprehensive lesson, students will learn how to enhance RAG systems using Deep Memory in conjunction with LLMs for applications within legal, financial, and biomedical sectors. Students will be guided through the process of preparing datasets, including gathering and chunking data as well as question and relevance score generation using LLMs. The lesson emphasizes the significant performance improvements offered by Deep Memory, such as an increase in retrieval accuracy without compromising search time, and demonstrates how to integrate and test this feature with real datasets—Legalbench, FinQA, and CORD-19. Additionally, students will gain insight into the practical implementation of Deep Memory through code examples and explore the advantages of Deep Memory over classic retrieval methods.