Why We Use Local Ollama 0.5 Instead of Cloud AI Models-Zero Data Privacy Risks for Healthcare Codebases

The provided scripts showcase how to leverage local AI models, specifically through the Ollama framework, for two distinct purposes: generating documentation and detecting PHI (Protected Health Information) in git diffs. Below is a detailed explanation of each script:

Script 1: Generating Documentation with Ollama

This script automates the process of generating Python documentation from docstrings using an AI model trained on programming tasks.

Key Components:

• Ollama Model: Utilizes a local instance of Ollama, which acts as a language model.
• Prompt Engineering: The prompt is carefully crafted to instruct the model to generate documentation based on provided code and comments.
• Structured Output: The script expects JSON-formatted output from the AI model.

Functionality:

1. Initialization:

   • Checks if the Ollama model is available.

2. Documentation Generation:

   • Takes a Python function or class as input, along with its docstring and comments.
   • Sends this information to the Ollama model via a prompt.
   • Receives structured documentation back from the AI.

3. Error Handling:

   • Catches exceptions related to API calls and malformed responses, ensuring robustness in deployment

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