Chod Matrix > Development > Data Science > AI Apps with ChatGPT and LangChain: The Introduction

4.54 out of 5

18 reviews on Udemy

AI Apps with ChatGPT and LangChain: The Introduction

Building Generative AI applications with powerful Large Language Models

Instructor:

Kris Celmer

249 students enrolled

English [Auto]

Understand Large Language Models and how to use them in AI Apps, how to prepare inputs, call GPT API endpoints and process outputs.

Core Prompting Techniques: Instructions, Zero-Shot, Few-Shot, Self Confidence, Chain-of-Thoughts, Output Formatting to JSON Strings, etc.

Using ChatGPT API in Natural Language Processing tasks: Named Entity Extraction, Classification, Sentiment Analysis, Translation.

Manage chat history with LangChain Conversation Memory classes, including Window Buffer, Token Buffer and Summary, enabling sophisticated, intelligent chatbots.

Use Embeddings to create Vector Databases and Semantic Similarity Search to retrieve relevant documents to chat with PDF or HTML docs and Source Code.

Summarise long texts with LangChain Map-Reduce and Refine Chains, including PDFs, web pages, YouTube video transcripts.

Use ReAct and OpenAI Functions Agent Classes to develop reasoning Agents capable of dealing with complex problems, requiring sequence of steps to solve.

Build Chat applications to perform ad hoc Data Analysis with Pandas dataframes or SQL databases.

Develop intuitions about how all these work and can be applied to solve real tasks.

Learn latest and greatest Python libraries to build LLM enabled applications, including the famous LangChain.

ChatGPT revolutionises businesses, how we work and greatly influences our lives. It is much more than a famous Web and mobile applications everyone is using now. Its creators recently released a publicly available API enabling creation of sophisticated AI Apps utilising the power of GPT models to most difficult Natural Language Processing tasks and beyond.

This course aims at Python developers to teach how to harness the power of latest and greatest Large Language Models in custom, innovative applications, how to interface existing data in various formats with ChatGPT available through the API.

You will learn the magic of LangChain – the Python Library delivering ever growing ecosystem of tools and integrations necessary to build the AI Apps. LangChain offers not only convenient wrappers around ChatGPT model APIs, but has plenty of ready-made classes and functions facilitating creation and use of Chat Memory, Vector DBs for semantic search of relevant documents, and blueprints of powerful Agents, capable of using Python functions in your environment to get access to local, proprietary data.

The course is very practical and consists of dozens of practical demonstrations of Python code solving various AI tasks. You will get detailed, precise and in-depth explanation of all presented concepts and algorithms.

All of the code used in the course is available for your download from GitHub repository. You can use it as a basis to further exploration and experimentation leading to quick and easy development of real-life AI Apps.

Course Introduction

Introduction

Downloading course materials and setting up the environment

Introduction to LLMs

Large Language Models

ChatGPT - a prime example of LLM

ChatGPT for Developers

Examples of tasks solved with ChatGPT

Getting started with OpenAI API and LangChain

Model variants in OpenAI API

Setting up an OpenAI API account

Using LangChain library to interact with OpenAI API

In-Context Learning and Action Planning design patterns

Overview of LangChain concepts

Prompt Engineering for Developers

Basic prompt tactics

Prompt techniques to improve output quality

Prompt Templates

Post-processing of ChatGPT responses

Introduction to Chains

Moderation techniques

Example AI App: Analysis of Customer Reviews

Chatbots

Closer look at Chat vs QA

Chat conversation memory

Interactive Chatbot in Jupyter notebook

Handling long chat memory

Documents and Web pages

Summary of long PDF document

Summary with Refine chain

Working with Web pages

Summarising YouTube video transcript

Naive QA with a PDF document

Vector Databases

Semantic search and Text embeddings

QA over a PDF document with Vector DB

QA with Pandas User Guide - Build Persistent Vector Database

QA with Pandas User Guide - Use Persistent Vector Database

QA with TikToken code

QA with Vector DB - Limitations

Action planning agents

Smooth intro to Agents

ReAct Agent internals

OpenAI Functions agent

OpenAI Functions agent with chat memory

Agent with Vector DB

Web search with Bing

Action planning and custom tools

CSV file with Pandas agent

SQL Database agent

Managing multiple Tools

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

4.5

4.5 out of 5

18 Ratings