Mô tả

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 Fundamentals

  • Setting Up the Environment with Dotenv: LangChain, Pinecone, OpenAI

  • LLM Models (Wrappers): GPT-3

  • ChatModels: GPT-3.5-Turbo and GPT-4

  • LangChain Prompt Templates

  • Simple Chains

  • Sequential Chains

  • Introduction to LangChain Agents

  • LangChain Agents in Action

  • Vector Embeddings

  • Introduction to Vector Databases

  • Diving into Pinecone

  • Diving into Chroma

  • Splitting and Embedding Text Using LangChain

  • Inserting the Embeddings into a Pinecone Index

  • Asking 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 Streamlit

  • Streamlit: main concepts, widgets, session state, callbacks


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!

Bạn sẽ học được gì

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.

Yêu cầu

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

Nội dung khoá học

14 sections

Getting Started

3 lectures
How to Get the Most Out of This Course
02:07
Join My Private Community!
00:25
Course Resources
00:15

Deep Dive into LangChain

15 lectures
LangChain Demo
05:27
Introduction to LangChain
07:20
Setting Up the Environment: LangChain, Python-dotenv
07:29
ChatModels: GPT-3.5-Turbo and GPT-4
06:30
Caching LLM Responses
04:58
LLM Streaming
02:55
Prompt Templates
05:35
ChatPrompt Templates
05:53
Simple Chains
06:55
Sequential Chains
07:13
Introduction to LangChain Agents
03:59
LangChain Agents in Action: Python REPL
07:40
LangChain Tools: DuckDuckGo and Wikipedia
11:07
Creating a ReAct Agent
13:31
Testing the ReAct Agent
04:49

LangChain and Vector Stores (Pinecone)

9 lectures
Short Recap of Embeddings
02:05
Introduction to Vector Databases
07:59
Authenticating to Pinecone
04:27
Working with Pinecone Indexes
09:23
Working with Vectors
08:43
Namespaces
06:42
Splitting and Embedding Text Using LangChain
09:03
Inserting the Embeddings into a Pinecone Index
08:49
Asking Questions (Similarity Search)
08:00

LangChain and Google's Gemini Pro and Pro Vision Models

7 lectures
Getting a Gemini API Key
04:20
Gemini Multimodal Models: Nano, Pro, and Ultra
05:13
Installing the Python Libraries for Gemini and Authenticating to Gemini
04:27
Integrating Gemini with LangChain
05:59
Using a System Prompt and Enabling Streaming
06:41
Multimodal AI With Gemini Pro Vision
10:36
Gemini Safety Settings
05:24

Jupyter AI

9 lectures
Jupyter AI
01:06
Python Version
00:15
Introduction to Jupyter AI and Other Coding Companions
03:39
Installing Jupyter AI
04:15
Using Jupyter AI in JupyterLab
12:26
Setting Up Jupyter AI in Jupyter Notebook
04:36
Using Jupyter AI in Jupyter Notebook
06:24
Using Interpolation for More Advanced Use Cases
05:24
Using Jupyter AI with Other Providers and Models
04:03

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

4 lectures
Project Introduction
03:03
Implementing a ChatGPT App with ChatPromptTemplates and Chains
10:44
Adding Chat Memory Using ConversationBufferMemory
06:44
Saving Chat Sessions
04:58

Project #2: RAG - Q&A App on Your Private Documents (Pinecone and Chroma)

10 lectures
Project Introduction
06:07
Loading Your Custom (Private) PDF Documents
07:26
Loading Different Document Formats
05:11
Public and Private Service Loaders
04:36
Chunking Strategies and Splitting the Documents
06:37
Embedding and Uploading to a Vector Database (Pinecone)
13:35
Asking and Getting Answers
10:40
Using Chroma as a Vector DB
11:09
Adding Memory to the RAG System (Chat History)
09:25
Using a Custom Prompt
08:08

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

8 lectures
LangChain Version
00:21
Project Introduction and Library Installation
05:26
Defining Functions
06:19
Creating the Sidebar
06:03
Reading, Chunking, and Embedding Data
06:22
Asking Questions and Getting Answers
05:26
Saving the Chat History
06:09
Clearing Session State History Using Callback Functions
05:01

Project #4: Summarizing With LangChain and OpenAI

10 lectures
Project Introduction
01:40
LangChain Version
00:15
Summarizing Using a Basic Prompt
05:32
Summarizing using Prompt Templates
05:03
Summarizing Using StuffDocumentsChain
06:00
Summarizing Large Documents Using map_reduce
06:06
map_reduce With Custom Prompts
04:53
Summarizing Using the refine CombineDocumentChain
07:43
refine With Custom Prompts
04:26
Summarizing Using LangChain Agents
05:11

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

4 lectures
Project Introduction
00:51
Building the App
11:29
Displaying the Chat History
08:22
Testing the App
03:20

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

13 lectures
Section Resources
00:16
Introduction to Streamlit
04:41
Streamlit Main Concepts
05:41
Displaying Data on the Screen: st.write() and Magic
05:45
Widgets, Part 1: text_input, number_input, button
05:11
Widgets, Part 2: checkbox, radio, select
07:33
Widgets, Part 3: slider, file_uploader, camera_input, image
10:28
Layout: Sidebar
01:58
Layout: Columns
05:32
Layout: Expander
02:11
Displaying a Progress Bar
04:00
Session State
09:07
Callbacks
07:12

[Appendix]: Python Programming

11 lectures
README
00:27
While and continue Statements
04:05
While and break Statements
05:35
List Slicing and Iteration
07:14
List Comprehension - Part 1
06:02
List Comprehension - Part 2
06:26
Working with Dictionaries
10:27
JSON Data Serialization
06:29
JSON Data Deserialization
05:36
Assignment: JSON and Requests/REST API
01:46
Assignment Answer: JSON and Requests/REST API
03:54

[Appendinx]: Setting Up the Environment: Jupyter Notebook and Google Colab

2 lectures
Setting Up the Environment: Jupyter Notebook
12:15
Setting Up the Environment: Google Colab
08:07

BONUS SECTION

2 lectures
Congratulations
00:20
BONUS: THANK YOU GIFT!
01:06

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