What you'll learn
Integrate ChatGPT into production-style apps with LangChain
Use LangChain components to build complex text generation pipelines
Enhance ChatGPT's output by automatically integrating user feedback
Teach ChatGPT new facts through Retrieval Augmented Generation
Extend LangChain to implement server-to-browser text streaming
Use OpenAI Plugins to add new capabilities to ChatGPT, such as database access and code execution
Understand every line of code we write so you can use these exact same techniques on your own projects
Build your own chat-with-a-PDF web application, complete with document upload and authentication
See how users interact with your chat features using observability and tracing
Requirements
Basic programming experience
Description
You've found the most advanced, most complete, and most intensive masterclass online for learning how to integrate LangChain and ChatGPT into production-ready applications!Thousands of engineers have learned how to build amazing applications using ChatGPT, and you can too. This course uses a time-tested, battle-proven method to make sure you understand exactly how ChatGPT works, and is the perfect pathway to help you get a new job as a software engineer working on AI-enabled apps.The difference between this course and all the others: you will go far beyond the basics of simple ChatGPT prompts, and understand how companies are integrating text generation into their apps today.___________ChatGPT is being used across industries to enhance applications with text generation. But with this new feature comes many challenges: Building complex text generation pipelines that incorporate outside informationCreating reusable configuration components that can be reassembled in different waysApplying user feedback (like upvotes/downvotes) to enhance ChatGPT's outputWiring in observability and tracing to see how users are interacting with your AIGenerate text performantly using distributed processingThis course will walk you through production-ready, repeatable techniques for addressing each of these challenges and many more.What will you build?This course focuses on creating a series of different projects of increasing complexity. You'll start from the very basics, understanding how to access ChatGPT 4 programatically. From there, we will quickly increase in complexity, building more complex projects with many more features. By the end, you will make a fully-featured web app that implements a "Chat-with-a-PDF" feature. Note: no previous web development experience is required.Here's a partial list of some of the topics you'll cover:Understand how complex text-generation pipelines workWrite reusable code using chains provided by LangChainConnect chains together in different ways to dramatically change your apps behavior with easeStore, retrieve, and summarize chat messages using conversational memoryImplement semantic search for Retrieval-Augmented Generation using embeddingsGenerate and store embeddings in vector databases like ChromaDB and PineconeUse retrievers to refine, reduce, and rank context documents, teaching ChatGPT new informationCreate agents to automatically accomplish tasks for you using goals you defineWrite tools and plugins to allow ChatGPT to access the outside worldMaintain a consistent focus on performance through distributed processing using Celery and RedisExtend LangChain to implement server-to-browser text streamingImprove ChatGPT's output quality through user-generated feedback mechanismsGet visibility into how users interact with your text generation features by using tracingThere are a ton of courses that show how to use ChatGPT at a very basic level. This is one of the very few courses online that goes far beyond the basics to teach you advanced techniques that top companies are using today. I have a passion for teaching topics the right way - the way that you'll actually use technology in the real world. Sign up today and join me!
Overview
Section 1: Let's Start - Dive In Here!
Lecture 1 How to Get Help
Lecture 2 What is LangChain?
Lecture 3 How a Typical AI-Enabled App Works
Lecture 4 Here It Is, This Is Why We Use LangChain
Section 2: ChatGPT and LangChain Integration
Lecture 5 Project Overview and Setup
Lecture 6 Creating an OpenAI API Key
Lecture 7 Using LangChain the Simple Way
Lecture 8 Introducing Chains
Lecture 9 Adding a Chain
Lecture 10 Parsing Command Line Arguments
Lecture 11 Securing the API Key
Lecture 12 Connecting Chains Together
Lecture 13 Chains in Series with SequentialChain
Section 3: Deep Dive into Interactions with Memory Management
Lecture 14 App Overview
Lecture 15 Receiving User Input
Lecture 16 Chat vs Completion Style Models
Lecture 17 Representing Messages with ChatPromptTemplates
Lecture 18 Implementing a Chat Chain
Lecture 19 Understanding Memory
Lecture 20 Using ChatBufferMemory to Store Conversations
Lecture 21 Saving and Extending Conversations
Lecture 22 Summarizations Conversation Summary Memory
Section 4: Adding Context with Embedding Techniques
Lecture 23 Project Overview
Lecture 24 Facts File Download
Lecture 25 Project Setup
Lecture 26 Loading Files with Document Loaders
Lecture 27 Search Criteria
Lecture 28 Introducing Embeddings
Lecture 29 The Entire Embedding Flow
Lecture 30 Chunking Text
Lecture 31 Generating Embeddings
Section 5: Custom Document Retrievers
Lecture 32 Introducing ChromaDB
Lecture 33 Building a Retrieval Chain
Lecture 34 What is a Retriever?
Lecture 35[Optional] Understanding Refine, MapReduce, and MapRerank
Lecture 36 Removing Duplicate Documents
Lecture 37 Creating a Custom Retriever
Lecture 38 Custom Retriever in Action
Lecture 39 Understanding Embeddings Download
Lecture 40 Visualizing Embeddings
Section 6: Empower ChatGPT with Tools and Agents
Lecture 41 App Overview
Lecture 42 Understanding Tools
Lecture 43 Understanding ChatGPT Functions
Lecture 44 SQLite Database Download
Lecture 45 Defining a Tool
Lecture 46 Defining an Agent and AgentExecutor
Lecture 47 Understanding Agents and AgentExecutors
Lecture 48 Shortcomings in ChatGPT's Assumptions
Lecture 49 Recovering from Errors in Tools
Lecture 50 Adding Table Context
Lecture 51 Adding a Table Description Tool
Lecture 52 Being Direct with System Messages
Lecture 53 Adding Better Descriptions for Tool Arguments
Lecture 54 Tools with Multiple Arguments
Lecture 55 Memory vs Agent Scratchpad
Lecture 56 Preserving Messages with Agent Executor
Lecture 57 Understanding Callbacks
Lecture 58 Implementing a Basic Callback Handler
Lecture 59 More Handler Implementaion
Section 7: Pinecone as a Vector Database
Lecture 60 App Overview
Lecture 61 Taking a Look at Mockups
Lecture 62 Boilerplate Download
Lecture 63 Boilerplate Setup
Lecture 64 How This App is Designed
Lecture 65 Outlining the First Feature
Lecture 66 Loading and Splitting From a PDF
Lecture 67 Sample PDF
Lecture 68 Testing the PDF Upload
Lecture 69 Introducing Pinecone
Lecture 70 Initializing the Pinecone Client
Lecture 71 Adding Documents to the Vector Store
Section 8: Distributed Text Generation with Celery
Lecture 72 Why is Processing Taking Forever?
Lecture 73 Introducing Background Jobs
Lecture 74 Redis Setup
Lecture 75 Redis - MacOS Setup
Lecture 76 Redis - Ubuntu and Windows Subsystem for Linux Setup
Lecture 77 Redis - Windows Setup *Without* WSL
Lecture 78 Adding in the Worker
Lecture 79 Queuing Up Jobs
Lecture 80 Updating Document Metadata
Section 9: Custom Message Histories
Lecture 81 Understanding the Apps Requirements
Lecture 82 Persistent Message Storage
Lecture 83 Introducing the Conversational Retrieval Chain
Lecture 84 Building the Retriever
Lecture 85 Custom History Objects
Lecture 86 Building a Custom SQL History
Lecture 87 Testing the Chain
Section 10: Streaming Text Generation
Lecture 88 Streaming Text Generation
Lecture 89 Creating a Working Playground
Lecture 90 Experimenting with a Streaming Language Model
Lecture 91 Chains Don't Want to Stream
Lecture 92 Receiving Chunks with a Callback
Lecture 93 Extending a LLM Chain
Lecture 94 Adding a Queue for Communication
Lecture 95 The Chain Really Wants to Wait
Lecture 96 Solving the Slow Chain
Lecture 97 It Works!
Lecture 98 Ending the Loop
Section 11: Extending LangChain
Lecture 99 Isolating the Queue and Handler
Lecture 100 Using a Mixin Approach
Lecture 101 Integrating the Streaming Code
Lecture 102 Testing the Streaming Setup
Lecture 103 Here's the Issue
Lecture 104 Isolating the Handler
Lecture 105 Streaming Complete!
Section 12: Self-Improving Text Generation
Lecture 106 Random Component Parts
Lecture 107 Component Part Flow
Lecture 108 Partial KWArg Application
Lecture 109 Building Component Maps
Lecture 110 Randomly Picking a Component
Lecture 111 Generalizing Component Picking
Lecture 112 Collecting User Feedback
Lecture 113 Redis Connection Setup
Lecture 114 Storing Votes in Redis
Lecture 115 Weighted Randomness
Lecture 116 Extracting Scores
Lecture 117 Calculating the Average Score
Lecture 118 Selecting Components By Score
Section 13: Implementing Tracing and Observability
Lecture 119 Adding Score Observability
Lecture 120 Building the Score Aggregate
Lecture 121 Adding Another Form of Memory
Lecture 122 Window Memory Implementation
Lecture 123 Text Generation Tracing
Lecture 124 Langfuse Signup
Lecture 125 Adding in Tracing
Lecture 126 Understanding the Trace
Lecture 127 Automatic Trace Creation
Software engineers looking to add AI into their apps

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rapidgator.net:

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