Tool Calling in LangChain

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Unlock the true power of Large Language Models! Are your LLM applications limited by static knowledge? Do you need your AI to interact with the real world, fetch real-time data, or perform actions?

Base LLMs are powerful text generators, but their capabilities expand exponentially when they can use external tools. In this in-depth tutorial, we dive deep into Tool Calling and Function Calling within the LangChain framework. This is your key to building dynamic, capable AI applications that can browse the web, perform calculations, interact with APIs, and so much more!

In this video, you will learn:

What "Tool Calling" and "Function Calling" mean in the context of LLMs and LangChain.
How to define and create simple *tools* in LangChain.
LangChain examples of using tools for real-world tasks.
Leveraging function calling APIs (like OpenAI's) within your LangChain applications.
Best practices for designing effective tools for your AI workflows.

Understanding tool and function calling is crucial for any developer looking to build sophisticated applications with LangChain and Large Language Models. This video provides the practical knowledge you need to take your *AI development* skills to the next level and build *intelligent agents* that can perform complex tasks.

🔥 If you're building with LangChain, this video is a must-watch!
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👇 Have questions about Tool/Function Calling or LangChain? Leave a comment below!
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Timestamps:
[00:00] Introduction to Tool/Function Calling
[06:46] Creating Tools with Functions
[24:00] Creating Tools with Pydantic

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