Building Generative AI Apps with LangChain

Leverage GenAI to Unlock Value from Business Data & Processes

Duration

2 Days

Level

Intermediate Level

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This course provides an overview into the world of generative artificial intelligence (Gen AI) and the use of large language models (LLMs). As a participant, you’ll explore the key concepts, techniques, and best practices involved in working with popular large language models.

LangChain is an open-source framework that allows developers working with AI to combine LLMs like GPT-4 with external sources of computation and data. It makes it easy to build and deploy AI applications that are both scalable and performant and can even work with confidential enterprise data securely.

This course also facilitates entry into the AI field for individuals from diverse backgrounds and enables the deployment of AI as a service. In this course, we’ll go over LangChain components, LLM wrappers, Chains, and Agents. We’ll also dive deep into embeddings and vector databases such as Pinecone that enable scaling Gen AI apps.

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This course on Generative AI and LangChain is designed to benefit individuals in various roles who are interested in working with LLMs:

Developers and AI professionals Learn LangChain to integrate LLMs with external resources for scalable AI applications.
Data scientists: Apply LangChain for custom document processing and analysis.
Product managers & AI Enthusiasts: Understand Generative AI capabilities and LangChain applications for informed decisions.

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Introduction

Getting Started – AI vs ML vs Generative AI
Understanding Generative AI
Key Step In Generative AI For Text – Predicting Next Word
Exploring Predictive Machine Learning vs Generative AI

Understanding LangChain with its Modules

Introduction to LangChain
Setting Up the Environment: LangChain, Pinecone, and Python-dotenv
LLM Models (Wrappers): GPT-3
ChatModels: GPT-3.50

Prompt, Chain & Agents

Prompt Templates
Simple Chains
Sequential Chains
Introduction to LangChain Agents
LangChain Agents in Action

Vector DB (Pinecone)

Short Recap of Embeddings
Introduction to Vector Databases
Diving into Pinecone, Part 2
Splitting and Embedding Text Using LangChain
Inserting the Embeddings into a Pinecone Index • Asking Questions (Similarity Search)

Code-Along #1: Using LangChain Modules Built a Streamlit Application

Hands-on #1 : Question-Answering Application on Your Custom (or Private) Documents

Project Introduction
Loading Your Custom (Private) PDF Documents
Loading Different Document Formats
Public and Private Service Loaders
Chunking Strategies and Splitting the Documents
Embedding and Uploading to a Vector Database (Pinecone)
Asking and Getting Answers
Adding Memory (Chat History)

Hands-on #2: Summarizing With LangChain and OpenAI

Project Introduction
Summarizing Using a Basic Prompt
Summarizing using Prompt Templates
Summarizing Using StuffDocumentsChain
Summarizing Large Documents Using map_reduce
map_reduce With Custom Prompts
Summarizing Using the refine CombineDocumentChain
refine With Custom Prompts
Summarizing Using LangChain Agents

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Programming in Python.
Fundamental concepts in AI or ML.
Familiarity with using command-line tools.

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Building Generative AI Apps with LangChain

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