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Welcome to this lesson on interacting with large language models using LangChain. In this module, we’ll explore the Model I/O layer—the essential bridge between your application and the LLM. By the end of this guide, you’ll be able to:
  • Craft effective prompts
  • Format and transform model responses
  • Parse and process output programmatically
The image is a welcome slide with a gradient background, listing three topics: understanding prompt engineering, formatting and transforming responses, and interacting with a large language model.
Model I/O focuses on three core capabilities:
  1. Prompt Invocation: Techniques for engineering prompts
  2. Response Formatting: Structuring outputs for downstream tasks
  3. Parsing & Transformation: Converting raw text into data structures

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Prerequisites

Before diving in, ensure you meet the following requirements:
The image is a slide titled "Prerequisites" with a blue gradient background, listing "Install the Libraries" and "Follow the Versions" as key points.
  • Python 3.8+ installed
  • An OpenAI API key stored in an environment variable
  • Exact versions of dependencies to avoid compatibility issues
LangChain is under active development. Installing the specified versions ensures consistency with this tutorial.

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Dependency Versions

PackageVersionInstallation Command
langchain0.1.10pip install langchain==0.1.10
langchain_openai0.0.8pip install langchain_openai==0.0.8
openai1.13.3pip install openai==1.13.3

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Installing Dependencies

Run the following commands in your terminal: 
pip install langchain==0.1.10
pip install langchain_openai==0.0.8
pip install openai==1.13.3

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Configuring Your API Key

Set your OpenAI API key as an environment variable: 
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY
If you’re using the KodeKloud labs environment, you can configure OPENAI_API_KEY directly through the labs interface. Refer to your lab’s documentation for details.

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Next Steps

With your environment ready, you can now:
  1. Invoke the LLM via prompt engineering
  2. Format responses into JSON, Markdown, or custom schemas
  3. Parse outputs into Python objects for further processing
For more information, see:

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