We may earn an affiliate commission when you visit our partners.
Harrison Chase
The landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes.
Read more
The landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes.
Read more
The landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes.
You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.
After taking this course, you’ll know how to:
- Generate structured output, including function calls, using LLMs;
- Use LCEL, which simplifies the customization of chains and agents, to build applications;
- Apply function calling to tasks like tagging and data extraction;
- Understand tool selection and routing using LangChain tools and LLM function calling – and much more.
- Start applying these new capabilities to build and improve your applications today.
Register for this course and see more details by visiting:
OpenCourser.com/course/55j8uv/functions
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Project Overview
What you’ll learn in this courseThe landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes. You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.After taking this course, you’ll know how to: (1) Generate structured output, including function calls, using LLMs; (2) Use LCEL, which simplifies the customization of chains and agents, to build applications; (3) Apply function calling to tasks like tagging and data extraction; (4) Understand tool selection and routing using LangChain tools and LLM function calling – and much more.Start applying these new capabilities to build and improve your applications today.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines latest advancements in LLMs, such as ChatGPT's function calling capability, keeping learners on the cutting edge
Introduces LangChain Expression Language (LCEL), a new syntax for customizing conversational agents and building applications
Provides hands-on experience in applying function calling to tasks like tagging, data extraction, tool selection, and routing
Taught by Harrison Chase, a recognized expert in the field of LLMs and conversational agents
Students are expected to have a basic understanding of LLMs and conversational agents
Save this course
Save Functions, Tools and Agents with LangChain to your list so you can find it easily later:
Save
Save
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 Functions, Tools and Agents with LangChain with these
activities:
Read 'Deep Learning for Coders with fastai & PyTorch'
Show steps
Gain a foundational understanding of deep learning and its practical applications, providing a strong base for the course.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting key concepts.
-
Work through the exercises and examples provided in the book.
Resource Compilation
Show steps
Enhance your understanding by gathering and reviewing all relevant course materials.
Show steps
-
Collect notes, assignments, and handouts.
-
Organize and categorize the resources.
-
Review the materials regularly.
Explore ChatGPT's capabilities
Show steps
Enhance your familiarity with the capabilities and limitations of ChatGPT, ensuring a stronger foundation for the course.
Browse courses on
LLMs
Show steps
-
Interact with ChatGPT to generate text, translate languages, write different types of creative content, and more.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Organize Course Resources
Show steps
Enhance your ability to effectively find, organize, and review course-related materials, facilitating efficient learning.
Browse courses on
Note-Taking
Show steps
-
Gather all relevant course materials, including lecture notes, assignments, and readings.
-
Organize your materials into a logical structure, using folders, subfolders, and naming conventions.
-
Review your organized materials regularly to reinforce your understanding.
LCEL Tutorial
Show steps
Enhance your ability to use LCEL by following these step-by-step tutorials.
Show steps
-
Watch the introductory video.
-
Follow the hands-on exercises.
Practice Function Calling in LLMs
Show steps
Engage in hands-on exercises and code challenges that require you to construct function calls using LLMs. This will improve your proficiency in generating structured output and enhance your ability to integrate LLMs into your applications.
Browse courses on
NLP
Show steps
-
Find online coding challenges or exercises that focus on LLM function calling
-
Practice writing function calls that generate structured output, such as JSON or XML
-
Build small projects that utilize LLM function calling for specific tasks
Develop a Conversational Agent Using LCEL
Show steps
Gain practical experience building conversational agents using LCEL, solidifying your understanding of its syntax and applications.
Browse courses on
LCEL
Show steps
-
Follow tutorials on LCEL to understand its syntax and capabilities.
-
Experiment with LCEL to create a simple conversational agent.
-
Enhance your agent by adding additional features and functionality using LCEL.
Explore Advanced Usage of LCEL
Show steps
Review the documentation and tutorials on LCEL to gain a deeper understanding of its syntax and advanced features. This will enable you to customize and extend your conversational agents with greater flexibility and power.
Browse courses on
LangChain
Show steps
-
Read the LangChain Expression Language documentation
-
Follow tutorials on LCEL syntax and function calling
-
Experiment with LCEL in your own conversational agent projects
Practice Function Calling with LLMs
Show steps
Improve your ability to generate structured output using LLMs, honing your skills in function calling.
Browse courses on
LLMs
Show steps
-
Identify tasks that can benefit from using function calling with LLMs.
-
Practice generating structured output, including function calls, using LLMs.
-
Experiment with different function calling syntax and techniques.
-
Troubleshoot and debug function calling issues.
Function Call Practice
Show steps
Deepen your understanding of function calling in LLMs by completing the exercises in this guided tutorial.
Browse courses on
Function Calling
Show steps
-
Review the provided examples.
-
Complete the practice exercises.
Attend an NLP Workshop
Show steps
Engage with experts and practitioners in the field of NLP, gaining valuable insights and expanding your knowledge beyond the classroom.
Browse courses on
NLP
Show steps
-
Research and identify NLP workshops that align with your interests and learning goals.
-
Register and attend the workshop, actively participating in discussions and hands-on exercises.
-
Follow up with the workshop organizers or speakers to continue learning and networking.
Function Calling Documentation
Show steps
Solidify your knowledge by creating a detailed guide on function calling techniques.
Browse courses on
Function Calling
Show steps
-
Gather information from course materials.
-
Organize your content.
-
Write the documentation.
Develop a Conversational Agent Using LCEL
Show steps
Design and implement a conversational agent that leverages LCEL to perform tasks such as tagging, extraction, and routing. This hands-on project will solidify your understanding of LCEL's capabilities and its practical applications.
Browse courses on
LCEL
Show steps
-
Define the scope and requirements of your conversational agent
-
Design the architecture of the agent, including the LCEL components
-
Develop the LCEL expressions and functions for the agent's core functionality
-
Test and refine the agent's performance
Conversational Agent Project
Show steps
Apply your newfound skills to build a chatbot that demonstrates function calling and conversational abilities.
Show steps
-
Plan the conversational flow.
-
Develop the LCEL code.
-
Test and refine the agent.
Read 'Deep Learning for Coders with fastai & PyTorch'
Show steps
Gain a foundational understanding of deep learning and its practical applications, providing a strong base for the course.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, taking notes and highlighting key concepts.
- Work through the exercises and examples provided in the book.
Resource Compilation
Show steps
Enhance your understanding by gathering and reviewing all relevant course materials.
Show steps
Show steps
Show steps
- Collect notes, assignments, and handouts.
- Organize and categorize the resources.
- Review the materials regularly.
Explore ChatGPT's capabilities
Show steps
Enhance your familiarity with the capabilities and limitations of ChatGPT, ensuring a stronger foundation for the course.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Interact with ChatGPT to generate text, translate languages, write different types of creative content, and more.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Organize Course Resources
Show steps
Enhance your ability to effectively find, organize, and review course-related materials, facilitating efficient learning.
Browse courses on
Note-Taking
Show steps
Show steps
Show steps
- Gather all relevant course materials, including lecture notes, assignments, and readings.
- Organize your materials into a logical structure, using folders, subfolders, and naming conventions.
- Review your organized materials regularly to reinforce your understanding.
LCEL Tutorial
Show steps
Enhance your ability to use LCEL by following these step-by-step tutorials.
Show steps
Show steps
Show steps
- Watch the introductory video.
- Follow the hands-on exercises.
Practice Function Calling in LLMs
Show steps
Engage in hands-on exercises and code challenges that require you to construct function calls using LLMs. This will improve your proficiency in generating structured output and enhance your ability to integrate LLMs into your applications.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Find online coding challenges or exercises that focus on LLM function calling
- Practice writing function calls that generate structured output, such as JSON or XML
- Build small projects that utilize LLM function calling for specific tasks
Develop a Conversational Agent Using LCEL
Show steps
Gain practical experience building conversational agents using LCEL, solidifying your understanding of its syntax and applications.
Browse courses on
LCEL
Show steps
Show steps
Show steps
- Follow tutorials on LCEL to understand its syntax and capabilities.
- Experiment with LCEL to create a simple conversational agent.
- Enhance your agent by adding additional features and functionality using LCEL.
Explore Advanced Usage of LCEL
Show steps
Review the documentation and tutorials on LCEL to gain a deeper understanding of its syntax and advanced features. This will enable you to customize and extend your conversational agents with greater flexibility and power.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Read the LangChain Expression Language documentation
- Follow tutorials on LCEL syntax and function calling
- Experiment with LCEL in your own conversational agent projects
Practice Function Calling with LLMs
Show steps
Improve your ability to generate structured output using LLMs, honing your skills in function calling.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Identify tasks that can benefit from using function calling with LLMs.
- Practice generating structured output, including function calls, using LLMs.
- Experiment with different function calling syntax and techniques.
- Troubleshoot and debug function calling issues.
Function Call Practice
Show steps
Deepen your understanding of function calling in LLMs by completing the exercises in this guided tutorial.
Browse courses on
Function Calling
Show steps
Show steps
Show steps
- Review the provided examples.
- Complete the practice exercises.
Attend an NLP Workshop
Show steps
Engage with experts and practitioners in the field of NLP, gaining valuable insights and expanding your knowledge beyond the classroom.
Browse courses on
NLP
Show steps
Show steps
Show steps
- Research and identify NLP workshops that align with your interests and learning goals.
- Register and attend the workshop, actively participating in discussions and hands-on exercises.
- Follow up with the workshop organizers or speakers to continue learning and networking.
Function Calling Documentation
Show steps
Solidify your knowledge by creating a detailed guide on function calling techniques.
Browse courses on
Function Calling
Show steps
Show steps
Show steps
- Gather information from course materials.
- Organize your content.
- Write the documentation.
Develop a Conversational Agent Using LCEL
Show steps
Design and implement a conversational agent that leverages LCEL to perform tasks such as tagging, extraction, and routing. This hands-on project will solidify your understanding of LCEL's capabilities and its practical applications.
Browse courses on
LCEL
Show steps
Show steps
Show steps
- Define the scope and requirements of your conversational agent
- Design the architecture of the agent, including the LCEL components
- Develop the LCEL expressions and functions for the agent's core functionality
- Test and refine the agent's performance
Conversational Agent Project
Show steps
Apply your newfound skills to build a chatbot that demonstrates function calling and conversational abilities.
Show steps
Show steps
Show steps
- Plan the conversational flow.
- Develop the LCEL code.
- Test and refine the agent.
Career center
Learners who complete Functions, Tools and Agents with LangChain will develop knowledge and skills
that may be useful to these careers:
Chatbot Developer
Chatbot Developer
A Chatbot Developer specializes in creating and supervising computer-generated conversational agents. Conversational agents simulate human conversation through text or speech. Over the next five years, the Chatbot Developer role is projected to grow by 22%. Students interested in a career in this field may find that the Functions, Tools and Agents with LangChain course is useful, as it provides a foundation in the use of LangChain, a conversational agent training and development platform. By learning how to build conversational agents, students can enter this high-growth field.
Language Model Developer
Language Model Developer
A Language Model Developer focuses on developing, maintaining, and evaluating natural language processing (NLP) models. A course like Functions, Tools and Agents with LangChain can help someone advance their career in Language Model Development by providing hands-on experience with building and using LLMs. Given that this course covers both the theoretical underpinnings of LLMs and their practical application, it may be very useful for someone interested in a career in Language Model Development.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, construct, and implement machine learning algorithms. The Functions, Tools and Agents with LangChain course is highly relevant to this career path. It provides hands-on experience with using and implementing LLMs, which are a critical component of modern machine learning algorithms. By understanding how to use LLMs effectively, one may be able to accelerate their career in Machine Learning Engineering.
Data Scientist
Data Scientist
Data Scientists are involved in the collection, analysis, and interpretation of data. This course may be useful for Data Scientists who wish to use LLMs to collect and analyze data. Additionally, the hands-on experience building and using conversational agents will help Data Scientists who wish to move into a management role.
Business Analyst
Business Analyst
A Business Analyst bridges the gap between technology and the business world. Conversational agents are quickly becoming an important part of modern business, and Business Analysts with experience in using and building conversational agents may be in high demand. This course may be useful for Business Analysts to learn how to use LLMs to automate tasks, generate insights, and build conversational agents for their clients.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software applications. The Functions, Tools and Agents with LangChain course would be useful for a Software Engineer interested in expanding their skill set to include conversational agent development. By understanding how to use and build conversational agents, a Software Engineer can offer a broader range of services to their clients.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of products. Conversational agents are increasingly being used to improve the customer experience, and Product Managers with knowledge of conversational agents may be in high demand. This course may be useful for Product Managers who want to learn how to use LLMs to improve their products and services.
Sales Manager
Sales Manager
Sales Managers are responsible for generating revenue and managing sales teams. Conversational agents can help Sales Managers automate tasks, qualify leads, and close deals. This course may be useful for Sales Managers who want to learn how to use LLMs to improve their sales process.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. Conversational agents can help Marketing Managers automate tasks, generate leads, and improve customer engagement. This course may be useful for Marketing Managers who want to learn how to use LLMs to improve their marketing campaigns.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. Conversational agents can help Customer Success Managers provide self-service support, answer questions, and resolve issues. This course may be useful for Customer Success Managers who want to learn how to use LLMs to improve their customer support.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. Conversational agents can help Operations Managers automate tasks, improve communication, and manage resources. This course may be useful for Operations Managers who want to learn how to use LLMs to improve their operations.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human capital of a business. Conversational agents can help Human Resources Managers automate tasks, recruit and hire candidates, and onboard new employees. This course may be useful for Human Resources Managers who want to learn how to use LLMs to improve their HR processes.
Financial Analyst
Financial Analyst
Financial Analysts provide businesses with financial advice and guidance. Conversational agents can help Financial Analysts automate tasks, analyze data, and generate reports. This course may be useful for Financial Analysts who want to learn how to use LLMs to improve their financial analysis.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. Conversational agents can help Consultants automate tasks, research topics, and generate reports. This course may be useful for Consultants who want to learn how to use LLMs to improve their consulting services.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. Conversational agents can help Entrepreneurs automate tasks, generate leads, and provide customer support. This course may be useful for Entrepreneurs who want to learn how to use LLMs to improve their businesses.
For more career information including salaries, visit:
OpenCourser.com/course/55j8uv/functions
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Functions, Tools and Agents with LangChain.
Save
Comprehensive guide to deep learning. It covers a wide range of topics, from the basics of deep learning to advanced topics such as convolutional neural networks and recurrent neural networks.
Save
Provides a comprehensive overview of deep learning, covering the latest advances in the field. It is an excellent resource for anyone who wants to learn more about deep learning, regardless of their background.
Save
Classic textbook on machine learning. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning.
Practical guide to deep learning with Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Comprehensive guide to natural language processing with deep learning. It covers a wide range of topics, from basic NLP concepts to advanced deep learning techniques.
Practical guide to natural language processing with Python. It covers a wide range of topics, from the basics of NLP to advanced topics such as machine learning and deep learning.
Classic textbook on speech and language processing. It covers a wide range of topics, from the basics of speech production and perception to advanced topics such as natural language understanding and generation.
Classic textbook on statistical learning. It covers a wide range of topics, from the basics of statistical learning to advanced topics such as support vector machines and random forests.
Comprehensive guide to pattern recognition and machine learning. It covers a wide range of topics, from the basics of pattern recognition to advanced topics such as Bayesian networks and Gaussian processes.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/55j8uv/functions
Share
Similar courses
Here are nine courses similar to
Functions, Tools and Agents with LangChain.
Function-Calling and Data Extraction with LLMs
OpenCourser.com/course/xrt04u/function
Function-Calling and Data Extraction with LLMs
Most relevant
AI-Agents: Automation & Business with LangChain & LLM Apps
OpenCourser.com/course/ghzj3p/ai
AI-Agents: Automation & Business with LangChain & LLM Apps
Most relevant
Open-source LLMs: Uncensored & secure AI locally with RAG
OpenCourser.com/course/w4klpt/open
Open-source LLMs: Uncensored & secure AI locally with RAG
Most relevant
KI-Agenten: Automation & Business mit LangChain & LLM Apps
OpenCourser.com/course/mhczvb/ki
KI-Agenten: Automation & Business mit LangChain & LLM Apps
Most relevant
LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps)
OpenCourser.com/course/h00vs5/langchain
LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps)
Most relevant
LangChain for LLM Application Development
OpenCourser.com/course/8lvjo3/langchain
LangChain for LLM Application Development
Most relevant
LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs
OpenCourser.com/course/he8bot/llm
LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI &...
Most relevant
Open-Source LLMs: Unzensierte & sichere KI lokal auf dem PC
OpenCourser.com/course/vf57pd/open
Open-Source LLMs: Unzensierte & sichere KI lokal auf dem...
Most relevant
Introduction to Large Language Models (LLMs) In Python
OpenCourser.com/course/iug9uf/introduction
Introduction to Large Language Models (LLMs) In Python
Most relevant
Effort
2
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Harrison Chase
Language
English
Good to know
Examines latest advancements in LLMs, such as ChatGPT's function calling capability, keeping learners on the cutting edge
Introduces LangChain Expression Language (LCEL), a new syntax for customizing conversational agents and building applications
Provides hands-on experience in applying function calling to tasks like tagging, data extraction, tool selection, and routing
Taught by Harrison Chase, a recognized expert in the field of LLMs and conversational agents
Students are expected to have a basic understanding of LLMs and conversational agents
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser