Magentic

Abstract

1. Introduction

Seamlessly integrate Large Language Models into Python code. Use the @prompt and @chatprompt decorators to create functions that return structured output from an LLM. Combine LLM queries and tool use with traditional Python code to build complex agentic systems.

2. Objective

Seamlessly integrate LLMs as Python functions

This project demonstrates how Python, OpenAI API can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• [Structured Outputs] using pydantic models and built-in python types.
• [Streaming] of structured outputs and function calls, to use them while being generated.
• [LLM-Assisted Retries] to improve LLM adherence to complex output schemas.
• [Observability] using OpenTelemetry, with native [Pydantic Logfire integration].
• [Type Annotations] to work nicely with linters and IDEs.
• [Configuration] options for multiple LLM providers including OpenAI, Anthropic, and Ollama.
• Many more features: [Chat Prompting], [Parallel Function Calling], [Vision], [Formatting], [Asyncio]...
• The functions argument to @prompt can contain async/coroutine functions. When the corresponding FunctionCall objects are called the result must be awaited.
• The Annotated type annotation can be used to provide descriptions and other metadata for function parameters. See the pydantic documentation on using Field to describe function arguments.
• The @prompt and @prompt_chain decorators also accept a model argument. You can pass an instance of OpenaiChatModel to use GPT4 or configure a different temperature. See below.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jackmpcollins/magentic.git
cd magentic

pip install magentic

uv add magentic

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by jackmpcollins and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.