только у нас скачать шаблон dle скачивать рекомендуем
Забыли пароль?

Фото видео монтаж » Видео уроки » Learn Langchain, Pinecone & Openai Build Next-Gen Llm Apps

Learn Langchain, Pinecone & Openai Build Next-Gen Llm Apps

 
Learn Langchain, Pinecone & Openai Build Next-Gen Llm Apps
Free Download Learn Langchain, Pinecone & Openai Build Next-Gen Llm Apps
Published 6/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.63 GB | Duration: 3h 40m
Unleash the Power of AI: Hands-On Applications with LangChain, Pinecone, and OpenAI. Join the AI Revolution Today!


What you'll learn
How to Use LangChain, Pinecone, and OpenAI to Build LLM-Powered Applications.
Learn about LangChain components, including LLM wrappers, prompt templates, chains, and agents.
Learn about the different types of chains available in LangChain, such as stuff, map_reduce, refine, and LangChain agents.
Acquire a solid understanding of embeddings and vector data stores.
Learn how to use embeddings and vector data stores to improve the performance of your LangChain applications.
Deep Dive into Pinecone.
Learn about Pinecone Indexes and Similarity Search.
Project: Build an LLM-powered question-answering application for custom or private documents.
Project: Build a summarization system for large documents using various methods and chains: stuff, map_reduce, refine, or LangChain Agents.
This will be a Learning-by-Doing Experience. We'll Build Together, Step-by-Step, Line-by-Line, Real-World Applications.
Requirements
Basic Python programming experience is required.
You should be able to sign up to OpenAI API with a valid phone number.
Description
Master LangChain, Pinecone, and OpenAI. Build hands-on generative LLM-powered applications with LangChain.The AI revolution is here and it will change the world! In a few years, the entire society will be reshaped by artificial intelligence.By the end of this course, you will have a solid understanding of the fundamentals of LangChain, Pinecone, and OpenAI.Currently, the effort, knowledge, and money of major technology corporations worldwide are being invested in AI.In this course, you'll learn how to build state-of-the-art LLM-powered applications with LangChain.This course is actually the second part of my "OpenAI API with Python Bootcamp" series.What is LangChain?LangChain is an open-source framework that allows developers working with AI to combine large language models (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.It 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 dive deep into embeddings and vector databases such as Pinecone.This will be a learning-by-doing experience. We'll build together, step-by-step, line-by-line, real-world LLM applications with Python, LangChain, and OpenAI.We will develop an LLM-powered question-answering application using LangChain, Pinecone, and OpenAI for custom or private documents. This opens up an infinite number of practical use cases.We will also build a summarization system, which is a valuable tool for anyone who needs to summarize large amounts of text. This includes students, researchers, and business professionals.I will continue to add new projects that solve different problems. This course, and the technologies it covers, will always be under development and continuously updated.The topics covered in this OpenAI API with Python course are:LangChain FundamentalsSetting Up the Environment with Dotenv: LangChain, Pinecone, OpenAILLM Models (Wrappers): GPT-3ChatModels: GPT-3.5-Turbo and GPT-4LangChain Prompt TemplatesSimple ChainsSequential ChainsIntroduction to LangChain AgentsLangChain Agents in ActionVector EmbeddingsIntroduction to Vector DatabasesDiving into PineconeSplitting and Embedding Text Using LangChainInserting the Embeddings into a Pinecone IndexAsking Questions (Similarity Search) and Gettings Answers (GPT-4)Disclaimer: This LangChain course is not for complete beginners as it requires some essential Python programming experience.The skills you'll acquire will allow you to build and deploy real-world AI applications. I can't tell you how excited I am to teach you all these cutting-edge technologies.Come on board now, so that you are not left behind.I will see you in the course!
Overview
Section 1: Getting Started
Lecture 1 Course Resources
Section 2: Deep Dive into LangChain and Pinecone
Lecture 2 Introduction to LangChain
Lecture 3 Setting Up the Environment: LangChain, Pinecone, and Dotenv
Lecture 4 LLM Models (Wrappers): GPT-3
Lecture 5 ChatModels: GPT-3.5-Turbo and GPT-4
Lecture 6 Prompt Templates
Lecture 7 Simple Chains
Lecture 8 Sequential Chains
Lecture 9 Introduction to LangChain Agents
Lecture 10 LangChain Agents in Action
Lecture 11 Short Recap of Embeddings
Lecture 12 Introduction to Vector Databases
Lecture 13 Diving into Pinecone, Part 1
Lecture 14 Diving into Pinecone, Part 2
Lecture 15 Splitting and Embedding Text Using LangChain
Lecture 16 Inserting the Embeddings into a Pinecone Index
Lecture 17 Asking Questions (Similarity Search)
Section 3: Project #1: Question-Answering Application on Your Custom (or Private) Documents
Lecture 18 Project Introduction
Lecture 19 Loading Your Custom (Private) PDF Documents
Lecture 20 Loading Different Document Formats
Lecture 21 Public and Private Service Loaders
Lecture 22 Chunking Strategies and Splitting the Documents
Lecture 23 Embedding and Uploading to a Vector Database (Pinecone)
Lecture 24 Asking and Getting Answers
Lecture 25 Adding Memory (Chat History)
Section 4: Project #2: Summarizing With LangChain and OpenAI
Lecture 26 Project Introduction
Lecture 27 Summarizing Using a Basic Prompt
Lecture 28 Summarizing using Prompt Templates
Lecture 29 Summarizing Using StuffDocumentsChain
Lecture 30 Summarizing Large Documents Using map_reduce
Lecture 31 map_reduce With Custom Prompts
Lecture 32 Summarizing Using the refine CombineDocumentChain
Lecture 33 refine With Custom Prompts
Lecture 34 Summarizing Using LangChain Agents
Section 5: BONUS SECTION
Lecture 35 BONUS: THANK YOU GIFT!
Python programmers who want to build LLM-Powered Applications using LangChain, Pinecone and OpenAI.,Any technical person interested in the most disruptive technology of this decade.,Any programmer interested in AI.
Homepage
https://www.udemy.com/course/master-langchain-pinecone-openai-build-llm-applications/



Links are Interchangeable - Single Extraction
Poproshajka

Вернуться назад




Коментарии

Информация
Посетители, находящиеся в группе Гости, не могут оставлять комментарии к данной публикации.