Unlock the Power of Large Language Models: Mastering PDF Text Extraction and Analysis

This course is designed to teach you how to effectively interact with text-based documents using the power of large language models (LLMs). The focus will be on working with PDF documents, as they are the most common format encountered in the business landscape. However, the techniques you'll learn can be applied to any type of text-based document.

The course will start with building a basic retrieval pipeline and exploring its different components. From there, we'll dive into more advanced techniques, such as re-ranking, query expansion, multi-query retrieval, and hypothetical document embedding. We'll also cover how to combine semantic search with traditional keyword-based search, and explore the use of the Pyramid Document Retriever to expand the context retrieved by the embedding model.

The goal is not only to teach you what these different techniques are, but also when and why to use them. Throughout the course, we'll provide practical code examples to help you implement these techniques in your own projects. By the end of the course, you'll have a fully working Python package that you can use in your own work.

The course will primarily focus on using LLMs and embedding models from OpenAI, as it provides a simple API that will allow us to quickly build prototypes. However, towards the end of the course, we'll also explore how to use local LLMs and embedding models to run the entire pipeline locally, without relying on external APIs.

The intended audience for this course are SaaS founders, developers, executives, and hobbyists. To get the most out of this course, you'll need a background in Python. This course will help you transform your brilliant ideas into working prototypes and analyze thousands of documents in minutes, not days.

During this course, we will cover a wide range of topics related to interacting with text-based documents using the power of large language models (LLMs). The focus will be on working with PDF documents, as they are the most common format encountered in the business landscape.

We will start by building a basic retrieval pipeline and exploring its different components, implementing them in code. From there, we will dive into more advanced techniques to improve the performance of the retrieval pipeline, such as re-ranking, query expansion, and multi-query retrieval.

Additionally, we will explore techniques for generating hypothetical documents based on the problem you're working on, known as "hypothetical document embedding." We will also look at ways to combine multiple retrievals to enhance the performance of the retrieval pipeline, blending semantic search techniques with traditional keyword-based search.

Furthermore, we will cover the Pyramid Document Retriever, a technique that helps expand the context retrieved by the embedding model.

Throughout the course, the focus will not only be on understanding these different techniques but also on when and why to use them. We will provide practical code examples to demonstrate how to apply these techniques in various scenarios.

The course will initially cover these topics, but since the field of retrieval and generation using LLMs is constantly evolving, the course will be updated with new lectures and topics over time.

This course is designed to provide you with practical skills and knowledge to leverage the power of large language models (LLMs) in interacting with text-based documents, particularly PDFs. As a participant, you will learn how to build robust retrieval pipelines, apply advanced techniques like re-ranking, query expansion, and multi-query retrieval, and explore methods for generating hypothetical documents based on your specific needs.

The instructor, with a Ph.D. and over 7 years of industry experience in leading machine learning and AI teams, has a strong technical background and a passion for open-source projects. They have built systems powering tens of thousands of consumer devices and created one of the most popular open-source RAG projects, Local GPT, which has over 19,000 stars on GitHub.

By joining this course, you will have the opportunity to enhance your skills, innovate in your field, and connect with a community of like-minded professionals. The course will provide you with a fully working Python package that you can use in your own projects, and you will have access to a dedicated channel on the Prompt Engineering Discord server, where you can directly chat with the instructor and other fellow practitioners about the topics covered in the course and beyond.

The course will primarily focus on using large language models (LLMs) and embedding models from OpenAI. The reason for this is that OpenAI's API provides a simple and straightforward way to quickly build prototypes.

However, in the later part of the course, we will also explore how to use local LLMs and embedding models to run the entire pipeline locally, without relying on any external APIs. This will give you the flexibility to use the models of your choice and run the system completely offline.

The specific models we will be using include:

• OpenAI's GPT-3 and other LLMs for various text generation and understanding tasks
• OpenAI's embedding models for generating semantic representations of text
• Local LLM and embedding models, such as those from Hugging Face, to enable fully offline deployments

By the end of the course, you will have a solid understanding of how to leverage these models to build powerful text-based document processing applications, and you'll have a fully working Python package that you can use in your own projects.

This course on "Beyond Basics" is designed to equip you with the knowledge and skills to effectively interact with text-based documents using the power of large language models (LLMs). Whether you are a SaaS founder, developer, executive, or hobbyist, this course will provide you with practical techniques to transform your ideas into working prototypes and analyze vast amounts of documents in a fraction of the time.

Throughout the course, we will cover a wide range of topics, including building a basic retrieval pipeline, advanced techniques like re-ranking, query expansion, and multi-query retrieval. We will also explore document embedding and how to combine semantic search with traditional keyword-based search methods. Additionally, we will delve into the use of the Pyramid Document Retriever to expand the context retrieved by the embedding model.

The focus of this course is not only on understanding these techniques but also on when and why to use them. You will be provided with practical code examples to help you implement these strategies in your own projects. Furthermore, you will receive a fully-working Python package that you can utilize in your future endeavors.

The course will initially cover these core topics, but as the field of retrieval and generation (RAG) is constantly evolving, the course will be updated with new lectures and content to ensure you stay at the forefront of the latest advancements.

The instructor, with a Ph.D. and over seven years of industry experience in leading machine learning and AI teams, is passionate about open-source and has created one of the most popular open-source RAG projects, Local GPT, with over 19,000 stars on GitHub. You will have the opportunity to directly interact with the instructor and fellow practitioners through a dedicated channel on the Prompt Engineering Discord server, allowing you to enhance your skills, innovate in your field, and connect with a community of like-minded professionals.

Join us on this exciting journey to master the art of interacting with text-based documents using the power of LLMs and unlock new possibilities in your field.