Lesson 33 of 40 AI / ML Expert ⏱ 35 min

LLMs — OpenAI API & LangChain

Integrate GPT-4 and other LLMs — streaming responses, function calling, RAG with vector databases, and building AI agents with LangChain.

Part 1: Introduction to LLMs — OpenAI API & LangChain

Integrate GPT-4 and other LLMs — streaming responses, function calling, RAG with vector databases, and building AI agents with LangChain.

This lesson uses Python 3.13 features and follows best practices for development in Visual Studio 2026 with Copilot assistance.

Part 2: Core Concepts & Code Examples

# LLMs — OpenAI API & LangChain — Python 3.13 Example
from typing import Any

def main() -> None:
    """Entry point demonstrating lesson 33 concepts."""
    print(f"Lesson 33: LLMs — OpenAI API & LangChain")

if __name__ == "__main__":
    main()

Part 3: Best Practices & Patterns

Apply the patterns from this lesson consistently across your projects. Visual Studio 2026's Python IntelliSense, type checking integration, and GitHub Copilot will guide you toward idiomatic, production-ready Python 3.13 code.

Use type hints for all function signatures
Write docstrings with Args/Returns sections
Run ruff for linting, mypy for type checking
Test every function with at least one pytest test

Part 4: Next Steps

Practice these concepts hands-on, then continue to Lesson 34. Return to Python Tutorial Home to see the full curriculum, or visit VisualStudioTutor.com for Visual Studio 2026 guides.

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