Module 2 Wrap-up: Engineering the Model Layer

You have successfully navigated the "Model Layer" of LangChain. You now understand that a model in LangChain is not just a URL or a function—it is a Managed Abstraction that supports streaming, batching, and provider switching.

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Hands-on Exercise: The Responsive Summarizer

1. The Goal

Create a script that takes a list of 5 long sentences and "Summarizes" them in parallel (using batch) but Streams the progress of the first sentence to the console.

2. The Implementation Plan

1. Create a List of 5 strings.
2. Use model.batch() for the 5 strings.
3. In a separate call (or first), use model.stream() for the most urgent string.

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Module 2 Summary

• Abstraction: We write to BaseChatModel, not to openai.Client.
• Chat Models: We use SystemMessage, HumanMessage, and AIMessage.
• Streaming: We use .stream() for real-time user feedback.
• Batching: We use .batch() for high-volume data processing.
• Agnosticism: We use factories to switch models instantly.

Coming Up Next...

In Module 3, we move to Prompts and Prompt Templates. We will learn how to turn our raw strings into reusable, versioned logic that powers our agents.

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Module 2 Checklist

• I can explain why ChatOpenAI is better than OpenAI.
• I have used the .stream() method in a local script.
• I understand the max_concurrency setting in .batch().
• I have successfully switched from OpenAI to a different provider (or a local model).
• I understand the difference between a message and a chunk.