LangChain RunnableLambda: Integrating Custom Python Logic into FCEL Chains | Video #19

Описание: What if you need to run a specific Python function in the middle of your AI pipeline? 🐍 In Video #19 of our LangChain Full Course, we master RunnableLambda.

While LangChain provides many built-in tools, real-world apps often require custom logic—like cleaning text, calling a custom API, or performing a calculation. I will show you how to wrap any Python function into a RunnableLambda so it can be used with the pipe (|) operator. This gives you 100% control over the data flowing through your FCEL (Functional Chain Expression Language) pipelines.

✅ In this tutorial, we cover:

What is a RunnableLambda? Turning standard Python functions into chain-linkable components.

The Power of Customization: When to use a Lambda instead of a standard OutputParser.

Synchronous vs. Asynchronous: How RunnableLambda handles different function types.

Data Transformation: Modifying the input before it reaches the Prompt or the LLM.

Practical Demo: Creating a function that processes text (like word counts or formatting) and plugging it directly into a RunnableSequence.

Why this is a Game Changer: Mastering RunnableLambda means you are no longer limited by the "out-of-the-box" features of LangChain. You can now bridge the gap between pure Python coding and AI orchestration.

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