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.
Show more
Show less

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

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Practical llm application development with langchain

According to learners, this course is a highly practical and up-to-date guide for building LLM-powered applications. Many found the hands-on, project-based approach to be a significant strength, helping to solidify understanding of complex topics like LangChain, Pinecone, and OpenAI. The instructor's effort in continuously updating content, especially the recent inclusion of Google's Gemini models, is widely praised. While most students appreciate the clear explanations, some note that the pace can be challenging for those with less experience in Python environments, and a few encountered minor code discrepancies. Overall, it's considered a comprehensive resource for developers aiming to implement real-world AI applications.

Provides useful introduction to building web interfaces for LLM apps.

"The Streamlit sections were very useful for deploying the apps."

"I learned how to create web interfaces (front-ends) for my LLM and generative AI apps using Streamlit, which was a practical addition."

"The Streamlit part was a bit basic, but it gave a good start to building front-ends."

Instructor explains complex LLM topics clearly and accessibly.

"The explanations are clear, but I found some parts moved a bit too fast for a beginner..."

"The lectures are well-paced and easy to follow. A must-take for anyone serious about LLMs."

"The instructor's explanations are clear and he makes complex topics accessible."

Continuously updated with the latest AI models and frameworks.

"This course is incredibly up-to-date and practical. The instructor clearly put a lot of effort into updating the content for Gemini and the latest LangChain versions."

"The recent update to include Gemini is a huge plus, making it relevant."

"I appreciate that the course covers the latest versions of LangChain and includes the new Gemini models."

Focuses on building real-world, hands-on LLM applications.

"Super practical! The instructor guides you step-by-step through building real applications."

"The hands-on projects are fantastic for solidifying understanding. I particularly enjoyed building the Q&A app."

"Excellent and comprehensive! The project-based approach truly helps in understanding complex concepts."

Some encountered minor code discrepancies or setup challenges.

"Some of the code snippets were hard to follow or had minor discrepancies with the latest library versions."

"While it says updated, I still encountered a few issues with the code."

"I had to debug a lot on my own regarding the environment setup."

Pace might be challenging, requiring strong Python environment skills.

"I found some parts moved a bit too fast for a beginner, even with prior Python knowledge."

"I struggled a lot with environment setup and dependency issues. The course assumes too much prior knowledge of Python environments."

"The course pace is just right for someone with a programming background, but maybe not for everyone like me."

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

Show steps

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

Show steps

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

Browse courses on Vector Databases

Show steps

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

Share Your LangChain Expertise as a Mentor

Show steps

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

Browse courses on LangChain

Show steps

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

Show steps

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

Browse courses on LangChain

Show steps

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

Show steps

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

Browse courses on LangChain

Show steps

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

Show steps

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

Browse courses on LangChain

Show steps

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

Show steps

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

Browse courses on LangChain

Show steps

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

Show steps

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

Browse courses on LangChain

Show steps

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:

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

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

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

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

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

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

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

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

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

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

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

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

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