Build Chat Applications With Openai And Langchain

Gain cutting-edge AI skills: Master the LangChain framework to build and deploy real-world AI applications

What you'll learn

Master LangChain to seamlessly integrate existing applications with potent Large Language Models (LLMs)

Learn to connect to OpenAI’s language and embedding models

Develop prompt engineering skills that improve performance and relevance of AI responses

Apply the state-of-the-art Retrieval Augmented Generation (RAG) technique to empower your AI-driven product with a knowledge base

Leverage AI to open up endless opportunities for your organization

Enhance your career prospects with rare and highly sought-after AI Engineering skills

Requirements

Intermediate Python coding skills are required

You need to have Jupyter Notebook up and running

Description

Are you an aspiring AI engineer excited to integrate AI into your product? Are you thrilled about the breakthroughs in the field of AI? Or maybe you’re eager to learn this new and exciting LangChain framework everyone’s talking about.If yes, then you’ve come to the right place!Why should you consider taking this LangChain course?In this Build Chat Applications with OpenAI and LangChain course, we’ll explore the increasingly popular LangChain Python library to develop engaging chatbot applications.With detailed, step-by-step guidance, you will use OpenAI’s API key to access their powerful large language models (LLMs). Once we have access to foundational models, we'll utilize LangChain and its integrations to create compelling prompts, add memory, input external data, and link it to third-party tools.LangChain's integration with third-party tools distinguishes it by enabling connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia, GitHub, Gmail, and more.None of this would be possible without mastering the LangChain Expression Language (LCEL)—essential for developing stateful, context-aware reasoning chatbots. These chatbots remember past conversations, answer questions about unseen data, and tackle more complex problems.Additionally, we’ll spend much of our time discussing the state-of-the-art Retrieval Augmented Generation (RAG), both theoretically and practically. This technique allows LLM-powered applications to analyze and answer questions about information outside their training data. Ultimately, we’ll create a chatbot that answers students’ questions on courses from the 365 library.What skills do you gain?- Integrate existing applications with powerful LLMs.- Connect to OpenAI’s language and embedding models using an OpenAI API key.- Develop prompt engineering techniques to enhance AI response performance and relevance.- Implement RAG to enrich your AI-driven product with a knowledge base.- Master the LCEL protocol—essential for developing applications with the LangChain Python library.- Connect external tools to your LLM-powered application.- Understand the mechanics behind agents and agent executors.Enhance your career prospects with rare and highly sought-after AI Engineering skills by enrolling in this LangChain and OpenAI course.Click ‘Buy Now’ and acquire real-world AI engineer skills today!

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction to the Course

Lecture 2 Business Applications of LangChain

Lecture 3 What Makes LangChain Powerful?

Lecture 4 What Does the Course Cover?

Section 2: Tokens, Models, and Prices

Lecture 5 Tokens

Lecture 6 Models and Prices

Section 3: Setting Up the Environment

Lecture 7 Setting Up a Custom Anaconda Environment for Jupyter Integration

Lecture 8 Obtaining an OpenAI API Key

Lecture 9 Setting the API Key as an Environment Variable

Section 4: The OpenAI API

Lecture 10 First Steps

Lecture 11 System, User, and Assistant Roles

Lecture 12 Creating a Sarcastic Chatbot

Lecture 13 Temperature, Max Tokens, and Streaming

Section 5: Model Inputs

Lecture 14 The LangChain Framework

Lecture 15 ChatOpenAI

Lecture 16 System and Human Messages

Lecture 17 AI Messages

Lecture 18 Prompt Templates and Prompt Values

Lecture 19 Chat Prompt Templates and Chat Prompt Values

Lecture 20 Few-Shot Chat Message Prompt Templates

Lecture 21 LLMChain

Section 6: Message History and Chatbot Memory

Lecture 22 Chat Message History

Lecture 23 Conversation Buffer Memory: Implementing the Setup

Lecture 24 Conversation Buffer Memory: Configuring the Chain

Lecture 25 Conversation Buffer Window Memory

Lecture 26 Conversation Summary Memory

Lecture 27 Combined Memory

Section 7: Output Parsers

Lecture 28 String Output Parser

Lecture 29 Comma-Separated List Output Parser

Lecture 30 Datetime Output Parser

Section 8: LangChain Expression Language (LCEL)

Lecture 31 Piping a Prompt, Model, and an Output Parser

Lecture 32 Batching

Lecture 33 Streaming

Lecture 34 The Runnable and RunnableSequence Classes

Lecture 35 Piping Chains and the RunnablePassthrough Class

Lecture 36 Graphing Runnables

Lecture 37 RunnableParallel

Lecture 38 Piping a RunnableParallel with Other Runnables

Lecture 39 RunnableLambda

Lecture 40 The @chain Decorator

Lecture 41 Adding Memory to a Chain (Part 1): Implementing the Setup

Lecture 42 RunnablePassthrough with Additional Keys

Lecture 43 Itemgetter

Lecture 44 Adding Memory to a Chain (Part 2): Creating the Chain

Section 9: Retrieval Augmented Generation (RAG)

Lecture 45 How to Integrate Custom Data into an LLM

Lecture 46 Introduction to RAG

Lecture 47 Introduction to Document Loading and Splitting

Lecture 48 Introduction to Document Embedding

Lecture 49 Introduction to Document Storing, Retrieval, and Generation

Lecture 50 Indexing: Document Loading with PyPDFLoader

Lecture 51 Indexing: Document Loading with Docx2txtLoader

Lecture 52 Indexing: Document Splitting with Character Text Splitter (Theory)

Lecture 53 Indexing: Document Splitting with Character Text Splitter (Code Along)

Lecture 54 Indexing: Document Splitting with Markdown Header Text Splitter

Lecture 55 Indexing: Text Embedding with OpenAI

Lecture 56 Indexing: Creating a Chroma Vector Store

Lecture 57 Indexing: Inspecting and Managing Documents in a Vector Store

Lecture 58 Retrieval: Similarity Search

Lecture 59 Retrieval: Maximal Marginal Relevance Search

Lecture 60 Retrieval: Vector Store-Backed Retriever

Lecture 61 Generation: Stuffing Documents

Lecture 62 Generation: Generating a Response

Section 10: Tools and Agents

Lecture 63 Introduction to Reasoning Chatbots

Lecture 64 Tools, Toolkits, Agents, and Agent Executors

Lecture 65 Fixing the GuessedAtParserWarning

Lecture 66 Creating a Wikipedia Tool and Piping It to a Chain

Lecture 67 Creating a Retriever and a Custom Tool

Lecture 68 LangChain Hub

Lecture 69 Creating a Tool Calling Agent and an Agent Executor

Lecture 70 AgentAction and AgentFinish

Aspiring AI engineers,Everyone who is serious about integrating AI into their product