Learn LangChain Pinecone OpenAI Build NextGen LLM Apps

Hands-On Applications with LangChain, Pinecone, and OpenAI. Build Web Apps with Streamlit. Join the AI Revolution Today!
Learn LangChain Pinecone OpenAI Build NextGen LLM Apps
File Size :
2.48 GB
Total length :
8h 24m



Andrei Dumitrescu


Last update




Learn LangChain Pinecone OpenAI Build NextGen LLM Apps

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 app with a modern web-based front-end 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 (including front-ends using Streamlit).
You’ll learn how to create web interfaces (front-ends) for your LLM and generative AI apps using Streamlit.
Streamlit: main concepts, widgets, session state, callbacks.
Learn how to use Jupyter AI efficiently.

Learn LangChain Pinecone OpenAI Build NextGen LLM Apps


Basic Python programming experience is required.
You should be able to sign up to OpenAI API with a valid phone number.


Master LangChain, Pinecone, and OpenAI. Build hands-on generative LLM-powered applications with LangChain.Create powerful web-based front-ends for your generative apps using Streamlit.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. You’ll also be able to create modern front-ends using Streamlit in pure Python.This LangChain course is the 2nd part of “OpenAI API with Python Bootcamp”. It is not recommended for complete beginners as it requires some essential Python programming experience.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.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. The applications will be complete and we’ll also contain a modern web app front-end using Streamlit.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 “LangChain, Pinecone and OpenAI” 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 PineconeDiving into ChromaSplitting and Embedding Text Using LangChainInserting the Embeddings into a Pinecone IndexAsking Questions (Similarity Search) and Gettings Answers (GPT-4)Proficient in using AI Coding Assistants (Jupyter AI)   Creating front-ends for LLM and generative AI apps using StreamlitStreamlit: main concepts, widgets, session state, callbacksThe 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!


Section 1: Getting Started

Lecture 1 How to Get the Most Out of This Course

Lecture 2 Join My Private Community!

Lecture 3 Course Resources

Section 2: Deep Dive into LangChain and Pinecone

Lecture 4 LangChain Demo

Lecture 5 Introduction to LangChain

Lecture 6 Setting Up the Environment: LangChain, Pinecone, and Python-dotenv

Lecture 7 LLM Models (Wrappers): GPT-3

Lecture 8 ChatModels: GPT-3.5-Turbo and GPT-4

Lecture 9 Prompt Templates

Lecture 10 Simple Chains

Lecture 11 Sequential Chains

Lecture 12 Introduction to LangChain Agents

Lecture 13 LangChain Agents in Action

Lecture 14 Short Recap of Embeddings

Lecture 15 Introduction to Vector Databases

Lecture 16 Diving into Pinecone, Part 1

Lecture 17 Diving into Pinecone, Part 2

Lecture 18 Splitting and Embedding Text Using LangChain

Lecture 19 Inserting the Embeddings into a Pinecone Index

Lecture 20 Asking Questions (Similarity Search)

Section 3: Jupyter AI

Lecture 21 Jupyter AI

Lecture 22 Introduction to Jupyter AI and Other Coding Companions

Lecture 23 Installing Jupyter AI

Lecture 24 Using Jupyter AI in JupyterLab

Lecture 25 Setting Up Jupyter AI in Jupyter Notebook

Lecture 26 Using Jupyter AI in Jupyter Notebook

Lecture 27 Using Interpolation for More Advanced Use Cases

Lecture 28 Using Jupyter AI with Other Providers and Models

Section 4: Project #1: Building a Custom ChatGPT App with LangChain From Scratch

Lecture 29 Project Introduction

Lecture 30 Implementing a ChatGPT App with ChatPromptTemplates and Chains

Lecture 31 Adding Chat Memory Using ConversationBufferMemory

Lecture 32 Saving Chat Sessions

Section 5: Project #2: Question-Answering Application on Your Custom (or Private) Documents

Lecture 33 Project Introduction

Lecture 34 Loading Your Custom (Private) PDF Documents

Lecture 35 Loading Different Document Formats

Lecture 36 Public and Private Service Loaders

Lecture 37 Chunking Strategies and Splitting the Documents

Lecture 38 Embedding and Uploading to a Vector Database (Pinecone)

Lecture 39 Asking and Getting Answers

Lecture 40 Adding Memory (Chat History)

Section 6: Project #3: Building a Front-End for the Question-Answering App Using Streamlit

Lecture 41 Project Introduction and Library Installation

Lecture 42 Defining Functions

Lecture 43 Creating the Sidebar

Lecture 44 Reading, Chunking, and Embedding Data

Lecture 45 Asking Questions and Getting Answers

Lecture 46 Saving the Chat History

Lecture 47 Clearing Session State History Using Callback Functions

Section 7: Project #4: Summarizing With LangChain and OpenAI

Lecture 48 Project Introduction

Lecture 49 Summarizing Using a Basic Prompt

Lecture 50 Summarizing using Prompt Templates

Lecture 51 Summarizing Using StuffDocumentsChain

Lecture 52 Summarizing Large Documents Using map_reduce

Lecture 53 map_reduce With Custom Prompts

Lecture 54 Summarizing Using the refine CombineDocumentChain

Lecture 55 refine With Custom Prompts

Lecture 56 Summarizing Using LangChain Agents

Section 8: Project #5: Building a Custom ChatGTP App with LangChain and Streamlit

Lecture 57 Project Introduction

Lecture 58 Building the App

Lecture 59 Displaying the Chat History

Lecture 60 Testing the App

Section 9: [Appendix]: Creating Web Interfaces for LLM Applications Using Streamlit

Lecture 61 Section Resources

Lecture 62 Introduction to Streamlit

Lecture 63 Streamlit Main Concepts

Lecture 64 Displaying Data on the Screen: st.write() and Magic

Lecture 65 Widgets, Part 1: text_input, number_input, button

Lecture 66 Widgets, Part 2: checkbox, radio, select

Lecture 67 Widgets, Part 3: slider, file_uploader, camera_input, image

Lecture 68 Layout: Sidebar

Lecture 69 Layout: Columns

Lecture 70 Layout: Expander

Lecture 71 Displaying a Progress Bar

Lecture 72 Session State

Lecture 73 Callbacks

Section 10: [Appendix]: Python Programming

Lecture 74 README

Lecture 75 While and continue Statements

Lecture 76 While and break Statements

Lecture 77 List Slicing and Iteration

Lecture 78 List Comprehension – Part 1

Lecture 79 List Comprehension – Part 2

Lecture 80 Working with Dictionaries

Lecture 81 JSON Data Serialization

Lecture 82 JSON Data Deserialization

Lecture 83 Assignment: JSON and Requests/REST API

Lecture 84 Assignment Answer: JSON and Requests/REST API

Section 11: [Appendinx]: Installing Jupyter Notebook and Google Colab

Lecture 85 Installing Jupyter Notebook and Google Colab


Lecture 86 Congratulations


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.

Course Information:

Udemy | English | 8h 24m | 2.48 GB
Created by: Andrei Dumitrescu

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