Building a Self-Healing Backend with AI + Docker

It sounds like you've developed a system where an AI-driven service can automatically detect and correct data issues in real-time. This is quite innovative! Let's break down the key components of your solution:

1. Real-Time Monitoring

Your system continuously monitors incoming data for specific patterns that indicate potential issues (e.g., malformed JSON, unexpected field types). You use regular expressions or pattern matching to identify these anomalies.

2. Normalization Layer

When an issue is detected, the AI adds a normalization layer before the strict validation process. This layer converts problematic data into a format that can be successfully validated by Pydantic models.

Example Code for Normalization

Here's a more detailed version of the normalization function you provided:

python
 
1import json
2
3def normalize_payload(raw: dict) -> dict:
4    """Unwrap MongoDB extended JSON and normalize shapes."""
5    
6    # Handle {"$numberLong": "..."} and {"$numberInt": "..."} wrappers
7    for field in ["long_value", "short_value", "integer_value"]:
8        val = raw.get(field)
9        if isinstance(val, dict):
10            raw[field] = int(val.get("$numberLong") or val.get("$numberInt", 0))
11
12[Read the full article at DEV Community](https://dev.to/kccab5b1/building-a-self-healing-backend-with-ai-docker-4pm4)
13
14---
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