Learn LangChain, Pinecone, OpenAI and Google's Gemini Models from Udemy

Fully updated in February 2024 for the latest versions of LangChain, OpenaAI, Google's Gemini and Pinecone.

Master LangChain, Pinecone, OpenAI and Google's Gemini. 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, OpenAI and Google's Gemini Pro and Pro Vision. 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, Google's Gemini
Google's Gemini Pro and Pro Vision
ChatModels: GPT-3.5-Turbo and GPT-4
LangChain Prompt Templates
Prompt Engineering using recommended Guidelines and Priciples
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.

What's inside

Learning objectives

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 using multimodal google's gemini pro vision
How to integrate google's gemini pro and pro vision ai models with langchain
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.
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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 using multimodal google's gemini pro vision
How to integrate google's gemini pro and pro vision ai models with langchain
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.
Show more
Show less

Syllabus

Getting Started

How to Get the Most Out of This Course

Join My Private Community!

Course Resources

Deep Dive into LangChain

LangChain Demo

Introduction to LangChain

Setting Up the Environment: LangChain, Python-dotenv

ChatModels: GPT-3.5-Turbo and GPT-4

Caching LLM Responses

LLM Streaming

Prompt Templates

ChatPrompt Templates

Simple Chains

Sequential Chains

Introduction to LangChain Agents

LangChain Agents in Action: Python REPL

LangChain Tools: DuckDuckGo and Wikipedia

Creating a ReAct Agent

Testing the ReAct Agent

LangChain and Vector Stores (Pinecone)

Short Recap of Embeddings

Introduction to Vector Databases

Authenticating to Pinecone

Working with Pinecone Indexes

Working with Vectors

Namespaces

Splitting and Embedding Text Using LangChain

Inserting the Embeddings into a Pinecone Index

Asking Questions (Similarity Search)

LangChain and Google's Gemini Pro and Pro Vision Models

Getting a Gemini API Key

Gemini Multimodal Models: Nano, Pro, and Ultra

Installing the Python Libraries for Gemini and Authenticating to Gemini

Integrating Gemini with LangChain

Using a System Prompt and Enabling Streaming

Multimodal AI With Gemini Pro Vision

Gemini Safety Settings

Jupyter AI

Python Version

Introduction to Jupyter AI and Other Coding Companions

Installing Jupyter AI

Using Jupyter AI in JupyterLab

Setting Up Jupyter AI in Jupyter Notebook

Using Jupyter AI in Jupyter Notebook

Using Interpolation for More Advanced Use Cases

Using Jupyter AI with Other Providers and Models

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

Project Introduction

Implementing a ChatGPT App with ChatPromptTemplates and Chains

Adding Chat Memory Using ConversationBufferMemory

Saving Chat Sessions

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

Loading Your Custom (Private) PDF Documents

Loading Different Document Formats

Public and Private Service Loaders

Chunking Strategies and Splitting the Documents

Embedding and Uploading to a Vector Database (Pinecone)

Asking and Getting Answers

Using Chroma as a Vector DB

Adding Memory to the RAG System (Chat History)

Using a Custom Prompt

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

LangChain Version

Project Introduction and Library Installation

Defining Functions

Creating the Sidebar

Reading, Chunking, and Embedding Data

Asking Questions and Getting Answers

Saving the Chat History

Clearing Session State History Using Callback Functions

Project #4: Summarizing With LangChain and OpenAI

Summarizing Using a Basic Prompt

Summarizing using Prompt Templates

Summarizing Using StuffDocumentsChain

Summarizing Large Documents Using map_reduce

map_reduce With Custom Prompts

Summarizing Using the refine CombineDocumentChain

refine With Custom Prompts

Summarizing Using LangChain Agents

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

Building the App

Displaying the Chat History

Testing the App

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

Section Resources

Introduction to Streamlit

Streamlit Main Concepts

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

Widgets, Part 1: text_input, number_input, button

Widgets, Part 2: checkbox, radio, select

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

Layout: Sidebar

Layout: Columns

Layout: Expander

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Andrei Dumitrescu from Crystal Mind Academy, who is recognized for their work in natural language processing (NLP) and machine learning (ML)

This course builds upon the 'OpenAI API with Python Bootcamp', demonstrating advanced use cases of generative AI using LLM (large language model) technology

Teaches the fundamentals of LangChain, Pinecone, Google's Gemini Pro and Pro Vision, enabling learners to build state-of-the-art LLM-powered applications

Provides a solid understanding of embeddings and vector data stores, essential concepts for optimizing AI applications

Offers hands-on, step-by-step guidance for building real-world applications, combining LLM and generative AI models with practical implementation

Utilizes a variety of learning formats, including code demonstrations and streamlit-based front-end development

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn LangChain, Pinecone, OpenAI and Google's Gemini Models with these activities:

Study 'Natural Language Processing with Python' by Steven Bird et al.

Show steps

Build a strong foundation in natural language processing, a core aspect of LangChain applications, by reviewing this comprehensive book.

View Natural Language Processing with Python:... on Amazon

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Read the book thoroughly, focusing on chapters relevant to LangChain
Take notes and highlight important concepts
Complete the exercises and assignments to reinforce your understanding

Review Concepts of Vector Databases

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Boost your understanding of vector databases and embeddings to enhance the performance of your LangChain applications.

Browse courses on Vector Databases

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Revisit lecture notes and textbooks on vector databases
Review articles and research papers on vector embeddings

Share Your LangChain Expertise as a Mentor

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Enhance your understanding of LangChain by guiding and assisting others in their learning journey.

Browse courses on LangChain

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Identify opportunities to mentor individuals interested in LangChain
Prepare materials and resources to support your mentees
Provide regular guidance and feedback to your mentees
Evaluate your mentoring experience and make adjustments as needed

Five other activities

Expand to see all activities and additional details

Show all eight activities

Contribute to the LangChain Community

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Gain practical experience and expand your network within the LangChain community by actively participating in its initiatives.

Browse courses on LangChain

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Join the LangChain online community (e.g., Discord, Slack)
Attend virtual events and meetups related to LangChain
Contribute to LangChain projects or discussions

Learn to Use LangChain with Google's Gemini

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Follow LangChain tutorials to learn more about using Google's Gemini with LangChain to enhance your applications

Browse courses on LangChain

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Read the tutorial on LangChain and Google's Gemini
Experiment with the code examples provided in the tutorial
Build a small project using LangChain and Google's Gemini

LangChain Prompt Engineering Exercises

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Complete exercises and practice prompt engineering techniques to improve your skills in crafting effective prompts for LangChain applications

Browse courses on LangChain

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Review the best practices for prompt engineering
Complete a series of exercises in prompt engineering
Use your improved prompts in your LangChain applications

Build a LangChain-Powered Summarization System

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Construct a project that leverages LangChain to summarize large text documents.

Browse courses on LangChain

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Design the architecture of your summarization system
Implement the system using LangChain
Test and evaluate the performance of your system

Develop a Conversational AI Assistant with LangChain

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Design and implement a sophisticated conversational AI assistant utilizing LangChain's capabilities

Browse courses on LangChain

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Define the requirements and specifications for your AI assistant
Build the core functionality of your AI assistant using LangChain
Train and fine-tune your AI assistant
Deploy your AI assistant and evaluate its performance

Career center

Learners who complete Learn LangChain, Pinecone, OpenAI and Google's Gemini Models will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers are responsible for designing, developing, and deploying computer vision models. This course provides a comprehensive overview of the computer vision landscape, including the latest advances in object detection and image segmentation. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy computer vision models that can solve real-world problems.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a comprehensive overview of the machine learning landscape, including the latest advances in NLP and computer vision. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy state-of-the-art machine learning models that can solve real-world problems.

See salaries and explore the career path for Machine Learning Engineer

NLP Engineer

NLP Engineers are responsible for designing, developing, and deploying NLP models. This course provides a comprehensive overview of the NLP landscape, including the latest advances in text classification and generation. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy NLP models that can solve real-world problems.

See salaries and explore the career path for NLP Engineer

AI Engineer

AI Engineers are responsible for designing, developing, and deploying AI systems. This course provides a comprehensive overview of the AI landscape, including the latest advances in NLP and computer vision. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI systems that can solve real-world problems.

See salaries and explore the career path for AI Engineer

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course provides a solid foundation in the use of LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, which are essential tools for data scientists. By learning how to use these technologies, you will be able to build and deploy powerful AI applications that can help you to improve your organization's performance.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course provides a solid foundation in the use of LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, which are essential tools for data analysts. By learning how to use these technologies, you will be able to build and deploy powerful AI applications that can help you to improve your organization's performance.

See salaries and explore the career path for Data Analyst

Back-End Developer

Back-End Developers are responsible for developing the back-end of websites and applications. This course provides a comprehensive overview of the back-end development landscape, including the latest advances in Java, Python, and Node.js. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve the back-end of your websites and applications

See salaries and explore the career path for Back-End Developer

UI Designer

UI Designers are responsible for designing the user interface of products and services. This course provides a comprehensive overview of the UI design landscape, including the latest advances in visual design and typography. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve the user interface of your products and services

See salaries and explore the career path for UI Designer

Product Manager

Product Managers are responsible for managing the development and launch of new products. This course provides a comprehensive overview of the product management landscape, including the latest advances in user experience design and market research. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve your product's performance.

See salaries and explore the career path for Product Manager

Software Engineer

Software Engineers are responsible for designing, developing, and deploying software applications. This course provides a comprehensive overview of the software engineering landscape, including the latest advances in cloud computing and big data. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy software applications that can solve real-world problems.

See salaries and explore the career path for Software Engineer

Full-Stack Developer

Full-Stack Developers are responsible for developing both the front-end and back-end of websites and applications. This course provides a comprehensive overview of the full-stack development landscape, including the latest advances in HTML, CSS, JavaScript, Java, Python, and Node.js. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve the performance of your websites and applications

See salaries and explore the career path for Full-Stack Developer

Project Manager

Project Managers are responsible for planning, executing, and closing projects. This course provides a comprehensive overview of the project management landscape, including the latest advances in agile development and risk management. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve your project's performance.

See salaries and explore the career path for Project Manager

UX Designer

UX Designers are responsible for designing the user experience of products and services. This course provides a comprehensive overview of the UX design landscape, including the latest advances in human-computer interaction and information architecture. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve the user experience of your products and services

See salaries and explore the career path for UX Designer

Front-End Developer

Front-End Developers are responsible for developing the front-end of websites and applications. This course provides a comprehensive overview of the front-end development landscape, including the latest advances in HTML, CSS, and JavaScript. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve the front-end of your websites and applications

See salaries and explore the career path for Front-End Developer

Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course provides a comprehensive overview of the business analysis landscape, including the latest advances in data mining and predictive analytics. By learning how to use LangChain, Pinecone, OpenAI, and Google's Gemini Pro and Pro Vision, you will be able to build and deploy AI applications that can help you to improve your organization's performance.

See salaries and explore the career path for Business Analyst

Learn LangChain, Pinecone, OpenAI and Google's Gemini Models

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