🚀 Proposal: Evolving PyFlunt into an AI-Powered Validation Tool

[fazedordecodigo]

Hello community!

PyFlunt has established itself as a robust library for implementing the Domain Notification Pattern in Python, excelling at syntactic validation and reducing code complexity (cyclomatic complexity) by avoiding excessive exceptions.

However, as software development evolves, so does the need for smarter validations. We are planning to take PyFlunt to the next level by integrating Artificial Intelligence capabilities.

The goal is to move from purely Deterministic Validation (e.g., regex, null checks) to Semantic Validation (e.g., context, intent, sentiment).

Here are the core concepts we are exploring:

1. Semantic Validation (AIContract)
Extending the standard Contract to support LLM-based checks. This would allow validations such as:

• is_offensive(text): Checks for toxicity or hate speech.
• is_coherent(summary, context): Validates if a summary actually matches the provided context.
• is_sentiment_positive(feedback): Ensures the tone matches the expectation.

2. AI-Driven Code Generation
A CLI tool or decorator that reads your Data Classes or Pydantic Models and automatically generates the boilerplate PyFlunt contracts based on field names and types, inferring business rules (e.g., "age" implies > 0, "email" implies is_email).

3. Smart & Humanized Notifications
Instead of returning a raw list of static error messages, an AI module could aggregate validation failures and rewrite them into a single, user-friendly, and helpful paragraph for the end-user.

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We want your feedback!

This is a major step, and we want to build this with the community.

• Which of these features would add the most value to your workflow?
• Do you have concerns about latency or dependencies?
• Do you have other ideas on how AI can improve validations?

Let's brainstorm in the comments below! 👇