Extract Structured Data from Text with Python & LLMs

In this tutorial, learn how to effortlessly convert unstructured text into structured data using Python and Large Language Models (LLMs). We’ll show you how to leverage a local LLM setup with Ollama, featuring Meta’s Llama 3.2 and IBM’s Granite 3.2, to extract key information from support tickets and other text data.

You'll discover how to generate clean JSON outputs from raw text and enforce data structure using Pydantic’s BaseModel and model_json_schema(). Plus, we’ll share tips on prompt engineering to improve accuracy and demonstrate how these powerful tools can streamline data cleaning and transformation.

By the end of this tutorial, you’ll know how to:

Extract structured data from unstructured text using local LLMs
Use Pydantic to validate AI-generated data
Optimize data parsing with Llama 3.2 and Granite 3.2
Apply Python techniques to enhance your data science workflow

Whether you're working with support tickets, customer messages, or other unstructured text, this guide will help you automate and optimize your data extraction process.

Links mentioned
Ollama Software: https://ollama.com
Python Package: https://pypi.org/project/ollama/
Granite3.2 Model: https://ollama.com/library/granite3.2
Llama3.2 Model: https://ollama.com/library/llama3.2
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