About this Video

• Video Title: LLM Function Calling - AI Tools Deep Dive
• Channel: Adam Lucek
• Speakers: Adam Lucek
• Duration: 00:31:14

Introduction

This video provides a comprehensive tutorial on LLM function calling, demonstrating how to connect language models to external tools, functions, or APIs. The tutorial covers basic function calling concepts, creating custom tools using OpenAI and Langchain, and building more advanced functions with Pydantic schemas. The video also explores the use of function calling in creating agentic systems.

Key Takeaways

• Function Calling Explained: LLM function calling allows language models to interact with external resources, extending their capabilities beyond text generation. The LLM determines when and which tools to use, generating a structured response that specifies the function and its arguments. The user then executes the function and feeds the results back to the LLM.
• Custom Tool Creation: The video demonstrates creating custom tools using OpenAI's API and Langchain. OpenAI requires defining a JSON schema specifying the tool's name, description, and parameters. Langchain offers a more standardized approach, simplifying tool definition and integration with various LLMs.
• Advanced Tools with Pydantic: Using Pydantic schemas simplifies the creation of complex functions with many parameters, improving organization and readability compared to raw JSON.
• Agentic Systems: Function calling is a key component of building agentic systems, where LLMs use tools and reasoning to fulfill user requests. The video shows how Langchain facilitates creating agents that automatically manage tool invocation and context management.
• Langchain for Standardization: Langchain provides a standardized framework for defining and using tools across different LLMs and providers, enabling easy plug-and-play functionality.

1. What are the three main ways to customize the default behavior of function calling using the tool_choice parameter in OpenAI's API?
2. Describe the process of executing functions and feeding the results back into the language model, including the use of tool messages.
3. How does Langchain's bind_tools method simplify the process of integrating tools with different LLMs compared to using OpenAI's API directly?
4. What are the key differences between Langchain's create_tool_calling_agent and create_react_agent functions, and how do they differ in their approach to managing tools and reasoning?
5. Ask anything...