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Function-Calling and Data Extraction with LLMs

Jiantao Jiao and Venkat Srinivasan

This course will teach you two critical skills for building applications with LLMs: function-calling and structured data extraction.

Function-calling allows you to extend LLMs with custom capabilities by enabling them to form calls to external functions based on natural language instructions. Structured data extraction enables LLMs to pull usable information from unstructured text.

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This course will teach you two critical skills for building applications with LLMs: function-calling and structured data extraction.

Function-calling allows you to extend LLMs with custom capabilities by enabling them to form calls to external functions based on natural language instructions. Structured data extraction enables LLMs to pull usable information from unstructured text.

You’ll work with NexusRavenV2-13B, an open source model fine-tuned for function-calling and data extraction. The model, available on Hugging Face, has outperformed GPT-4 in some function-calling tasks, and has 13 billion parameters so it can be hosted locally.

What you’ll explore:

1. Learn how you can use function-calling in detail: form prompts with function definitions, and use an LLM response to call those functions.

2. Use an LLM with multiple function calls, including parallel and nested function calls. This allows you to create complex agent workflows where an LLM plans and executes a series of function calls to achieve a goal.

3. Use OpenAPI specifications to build function calls that can access web services.

4. Use function-calling to extract structured data from a natural language input.

5. Build an application that takes customer service transcripts, builds SQL calls, and stores results in a database with commands generated by the LLM.

The skills you’ll learn in this course will allow you to build advanced AI agents and assistants that can process and analyze customer feedback, automate data entry and content management workflows, enhance search and recommendation systems with structured data, and many other real-world applications.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Highly relevant to industry by providing the skills and knowledge needed to create LLMs with custom capabilities
Taught by Jiantao Jiao and Venkat Srinivasan, who have contributed significantly to LLM development and research
Covers topics that are essential for building advanced AI agents and assistants
Develops function-calling and structured data extraction skills, which are critical for LLM applications
Employs a practical approach with hands-on exercises and real-world case studies
Lacks prerequisites and is designed for learners with a background in natural language processing and AI

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Reviews summary

Practical llm function-calling & data extraction

According to learners, this course offers a practical, hands-on deep dive into function-calling and structured data extraction with LLMs. Students particularly appreciate the clear explanations and effective demonstrations, especially when working with the open-source NexusRavenV2-13B model. Many found the real-world application examples, such as building SQL calls from customer transcripts, to be highly valuable for immediate professional use. While generally well-structured and easy to follow, some noted it's best for those with basic Python and LLM familiarity.
Beneficial for those with existing Python and LLM basics.
"I found it helpful that I had some prior experience with Python and general LLM concepts before starting."
"While very good, a foundational understanding of LLMs and Python would make the course even smoother."
"It moves at a good pace, but absolute beginners might need to brush up on basics first."
Leverages NexusRavenV2-13B, beneficial for local hosting and accessibility.
"Using NexusRavenV2-13B was a big plus for me, as I prefer working with open-source models."
"It's great that the course focuses on a model that can be hosted locally, making it more accessible."
"I appreciate the choice of NexusRavenV2-13B, it's a powerful open-source alternative to proprietary models."
Code demonstrations and labs are highly effective learning tools.
"The coding demos were exceptionally helpful in understanding how to implement the concepts."
"I really appreciated the interactive labs which solidified my understanding of function-calling."
"The practical exercises truly reinforced the theoretical knowledge presented in the lectures."
Complex concepts like function-calling are explained with great clarity.
"The instructor does an excellent job of simplifying complex topics into digestible lessons."
"I found the explanations on parallel and nested function calls particularly clear and easy to grasp."
"The course breaks down advanced LLM techniques into understandable parts."
Focuses on immediate, real-world application through practical exercises.
"The hands-on coding and projects are the strongest part of the course for me; I can immediately apply these skills."
"I learned how to use practical tools and strategies that I could apply immediately to my work with LLMs."
"This course provides a strong foundation for building real-world AI agents and assistants."

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 Function-Calling and Data Extraction with LLMs with these activities:
Mentorship Program
Seek out mentors who can guide your learning journey and provide valuable insights.
Show steps
  • Identify potential mentors who have experience and expertise in function-calling and data extraction.
  • Reach out to potential mentors and introduce yourself.
  • Request guidance and support in your learning journey.
Calculus Review
Reinforce your understanding of calculus concepts covered in this course.
Browse courses on Calculus
Show steps
  • Review your calculus notes from previous coursework.
  • Work through practice problems to test your understanding of key concepts.
Resource Compilation
Organize and consolidate valuable resources related to function-calling and data extraction.
Show steps
  • Identify and gather resources such as articles, tutorials, and code examples related to function-calling and data extraction.
  • Organize the resources into a structured and accessible format.
  • Share your compilation with other learners to support their learning journey.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Guided Tutorials on Function-Calling Syntax
Enhance your grasp of function-calling syntax through guided tutorials.
Show steps
  • Locate online tutorials or resources that provide step-by-step guidance on function-calling syntax.
  • Follow the instructions and practice writing function calls.
  • Test your understanding by applying the syntax to sample problems.
Function-Calling Exercises
Solidify your understanding of function-calling through repetitive exercises.
Show steps
  • Find online platforms or textbooks that offer function-calling exercises.
  • Practice writing function calls to solve a variety of problems.
  • Review your solutions and identify areas for improvement.
Contributing to Open Source Projects
Gain hands-on experience and contribute to the open source community while enhancing your skills.
Show steps
  • Identify open source projects related to function-calling or data extraction.
  • Review the project's documentation and contribution guidelines.
  • Find an area where you can contribute your skills and knowledge.
  • Submit a pull request or issue to the project.
Function-Calling Project
Apply your function-calling skills to develop a practical project.
Show steps
  • Identify a problem or task that can be solved using function-calling.
  • Design and implement a solution using function-calling techniques.
  • Test and refine your project to ensure it meets the desired requirements.
Structured Data Extraction Report
Demonstrate your proficiency in structured data extraction by creating a comprehensive report.
Show steps
  • Collect a dataset containing unstructured text.
  • Develop a structured data extraction strategy using function-calling techniques.
  • Implement your strategy to extract structured data from the dataset.
  • Create a report that presents your findings and insights from the extracted data.
Mentoring Sessions
Share your knowledge and support fellow learners by providing mentoring sessions.
Show steps
  • Identify opportunities to assist other students in understanding function-calling concepts.
  • Offer your assistance and guidance to learners who are struggling or have questions.
  • Provide constructive feedback and support to help learners improve their understanding and skills.

Career center

Learners who complete Function-Calling and Data Extraction with LLMs will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Jiantao Jiao
Venkat Srinivasan
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Highly relevant to industry by providing the skills and knowledge needed to create LLMs with custom capabilities
Taught by Jiantao Jiao and Venkat Srinivasan, who have contributed significantly to LLM development and research
Covers topics that are essential for building advanced AI agents and assistants
Develops function-calling and structured data extraction skills, which are critical for LLM applications
Employs a practical approach with hands-on exercises and real-world case studies
Lacks prerequisites and is designed for learners with a background in natural language processing and AI
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