Module 3 Introduction - Retrieval Augmented Generation Agents

The "Retrieval Augmented Generation Agents" module offers a comprehensive exploration into the creation and enhancement of AI agents, with a focus on integration and application in various fields. It begins by introducing LangChain, where students learn about agents, tools, and the initiation of OpenGPTs, gaining practical insights into setting up and customizing AI assistants. Then, it delves into the LlamaIndex framework, teaching students how to build efficient RAG systems by integrating OpenAI agents with various data sources and creating custom functions for enhanced decision-making. The module also covers the use of the OpenAI Assistants API and Hugging Face Inference API.

LangChain Overview: Agents, Tools, and OpenGPT Introduction 

In this lesson, students will learn about the fundamental concepts of LangChain, focusing on agents, tools, and the initiation of OpenGPTs. They will examine how agents integrate chains, prompts, memory, and tools to execute tasks and understand the different types of agents, such as Zero-shot ReAct and Conversational Agent, designed for various scenarios. The lesson also covers the available tools and how to customize them for specific needs, benefiting from functionalities like Python tool, JSON tool, and CSV tool. Additionally, students will get practical insights into setting up and creating LangChain OpenGPTs through cloning the repository and customizing prompts, providing a comprehensive understanding of how to configure and use AI assistants similarly to OpenAI GPTs for tailored interactions.

Utilizing AI Agents with the LlamaIndex Framework for Enhanced Decision-Making

In this lesson, students will learn how to leverage agents within the LlamaIndex framework to build a more efficient and insightful RAG (Retrieval-Augmented Generation) system. They will gain insights into integrating OpenAI agents with various data sources and create custom functions to enhance the agent's capabilities in areas such as mathematical operations. The lesson provides guidance on installing necessary packages, configuring API keys, defining data sources, employing query engines, and setting up agents. Students will also explore an interactive chat interface with the agent and the creation of a dataset, using custom functions as tools that the agent can invoke as required. Finally, students will gain exposure to LlamaHub for further expanding the functionalities of their agents.

Crafting AI Assistants via OpenAI and Hugging Face API

In this lesson, students will explore the capabilities of the OpenAI Assistants API, including the Code Interpreter, Knowledge Retrieval, and Function Calling features. The lesson offers a step-by-step guide for creating and configuring AI assistants integrating OpenAI's tools, revisiting fundamental concepts such as Threads, Messages, and Tools for individual interactions. Additionally, the lesson introduces other advanced models by OpenAI like Whisper, Dall-E 3, and GPT-4 Vision that can be valuable integrations for comprehensive AI product development. We also cover how to use the Hugging Face Inference API to leverage a broad spectrum of machine learning models for tasks like text summarization, sentiment analysis, and text-to-image generation. By the conclusion of the lesson, students will possess the necessary understanding to harness these tools for their own sophisticated AI projects.

Project; Multimodal Financial Document Analysis and Recall

In this lesson, students will learn how to use tools such as GPT-4 vision to enhance Retrieval-augmented Generation (RAG) for processing financial documents like Tesla's Q3 financial report PDF, involving the extraction of text, tables, and graphs, and transforming them into a query-able format using a vector database for efficient information retrieval by an AI chatbot. The lesson covers using tools such as Unstructured.io for text and table extraction, GPT-4V for graph information extraction, and the use of Deep Lake and LlamaIndex for storing and recalling the processed data to address user queries effectively. We also show how to use Deep Memory to enhance retrieval accuracy. The techniques detailed equip students to develop AI applications capable of analyzing and recalling complex multimodal data from financial documents.

Building a Smart Shopping Assistant with DeepLake and LlamaIndex

In this lesson, students will learn how to create an intelligent shopping assistant using AI technologies, specifically leveraging vector databases and frameworks like DeepLake and LlamaIndex. They will be guided through the processes of data collection, vector database population, development of core tools for query retrieval and outfit generation, system integration, and UI development. This lesson also explores the integration of weather data and temporal elements to enhance the system's recommendations, along with the challenges of debugging and deploying agent-based applications. Through hands-on experience and step-by-step demonstrations, students will gain practical skills to build and integrate AI components into a functional and interactive fashion recommendation tool.