Welcome to the first chapter! 👋🏻
In this chapter, we will cover the advanced RAG essentials - rerankers, smart chunking methods, hybrid search and much more. Chances are, you have probably heard of some of these - rerankers are not a new concept and BM25 is older than me. However, each chapter in this section tries to provide a comprehensive overview of a given area - you might have heard about cross-encoder rerankers, but have you seen what LLM rerankers can do? That being said, if you feel highly comfortable with a given area, feel free to just skim it. My favourite techniques described in this module (that are not that well known as of December 2024) are embedding adapters (chapter on fine-tuning) and contextual retrieval (in other notable techniques), I think you will love them!
Rerankers
In the chapter on rerankers, we will explore how different reranking techniques can improve the precision of RAG systems. You’ll learn how methods like cross-encoders, LLM rerankers, and ColBERT work to refine retrieved documents, ensuring the most relevant information is prioritized. This chapter also highlights when to use these methods based on factors like accuracy, scalability, and computational cost, giving you the tools to make informed choices for your RAG workflows.
Fine-tuning
In the chapter Fine-tuning embedding models, we focus on two key techniques to improve retrieval accuracy: fine-tuning and embedding adapters. Fine-tuning enables embedding models to align with domain-specific data, optimizing both query and document representations, though it requires re-indexing the entire corpus. Embedding adapters, on the other hand, offer a lightweight alternative by dynamically adjusting query embeddings without modifying the corpus, providing cost-effective improvements. This chapter highlights when and how to apply these approaches to achieve better precision in retrieval-augmented generation systems.
Hybrid search
This chapter explores the synergy of sparse and dense retrieval methods to enhance search precision and recall. Sparse retrieval, like BM25, excels at exact keyword matching, while dense retrieval uses embeddings to capture semantic nuances. By combining these techniques, hybrid search retrieves documents that are both keyword-specific and contextually relevant. This chapter explains how to integrate these methods and normalize their scores. Through practical examples and implementation tips, you’ll learn to leverage hybrid search for diverse, complex use cases in retrieval-augmented generation systems.
Advanced chunking
In this chapter, we focus on advanced methods for dividing text into segments, highlighting their impact on retrieval and generation in RAG pipelines. While basic chunking methods like splitting text by a fixed number of tokens are straightforward, they often disrupt logical flow and reduce retrieval accuracy. This chapter covers advanced techniques such as semantic chunking, late chunking, and sentence-window splitting, which aim to preserve context and improve the quality of both retrieval and responses.
Multimodal RAG
In this chapter, we focus on how RAG can integrate different types of data, such as text and images, to improve retrieval and response generation. Using an example with image data, we demonstrate how to build a multimodal retrieval pipeline that combines text and image embeddings for more contextually rich outputs. The chapter highlights the practical steps of working with images in RAG while mentioning the potential to extend these techniques to other modalities.
Other notable methods
This chapter explores a variety of cutting-edge methods that push the boundaries of traditional RAG systems. From contextual retrieval, which enriches document chunks with precise context using LLMs, to contextual chunk headers, a lightweight alternative for enhancing retrieval relevance, these techniques demonstrate the diverse ways to optimize performance and cost. Additionally, we explore ColBERT, an advanced architecture that performs token-level matching to capture granular semantic details. These methods may not demand full chapters, but they represent powerful tools for refining retrieval-augmented generation workflows and inspire deeper exploration.
Hybrid RAG project - restaurants
This chapter provides a hands-on exploration of implementing hybrid search systems by integrating BM25 and dense vector retrieval methods. While the earlier chapter on Hybrid Search focused on the conceptual underpinnings and toy examples, this chapter is all about code. Using a practical restaurant reviews dataset, we delve into the step-by-step process of setting up inverted and BM25 indexes, generating dense embeddings, and combining these techniques for a robust hybrid approach. You'll also see real-world challenges addressed, such as optimizing retrieval, asynchronous querying, and even integrating LLM-based answers for user queries. Whether you're looking to refine your implementation skills or enhance a retrieval pipeline, this chapter serves as a technical companion to the broader concepts covered earlier.
Let’s get this party started! 🥳
Final note: Most chapters contain practical takeaways at the end - a “So what?” section if you will. In that section I try to provide some high-level recommendations that you can use in practice. 💪🏻
Final Final note:
