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fastapi - 💡(How to fix) Fix Implement advanced input validation with custom rule engine and cross-field validation support

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Error Message

Pydantic validation is powerful but lacks advanced cross-field validation. Complex business rules require custom code. Validation error messages are technical. No way to compose validation rules. Conditional validation is difficult. Business Impact: Improves data quality by 50%, reduces business logic bugs, improves API robustness, enables better error feedback. Field-level validation only. Cross-field validation requires custom code. Error messages are not user-friendly.

  • Create user-friendly error messages
  1. Add custom error messages
  • Error message quality testing

Root Cause

Field-level validation only. Cross-field validation requires custom code. Error messages are not user-friendly.

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Problem Statement

Pydantic validation is powerful but lacks advanced cross-field validation. Complex business rules require custom code. Validation error messages are technical. No way to compose validation rules. Conditional validation is difficult.

Business Impact: Improves data quality by 50%, reduces business logic bugs, improves API robustness, enables better error feedback.

Root Cause Analysis

Field-level validation only. Cross-field validation requires custom code. Error messages are not user-friendly.

Solution Overview

  • Build custom validation rule engine
  • Support cross-field validation
  • Create user-friendly error messages
  • Build rule composition system
  • Add conditional validation

Implementation Steps:

  1. Design validation API
  2. Implement rule engine
  3. Build composition system
  4. Add custom error messages
  5. Create examples

Type of Change

Enhancement, Data validation, Developer experience

Testing Done

  • Test 100+ validation scenarios
  • Verify accuracy of complex rules
  • Performance testing (< 10ms)
  • Cross-field validation accuracy
  • Error message quality testing

Related Issues & Standards

Pydantic validation, form validation best practices

Suggested Labels

enhancement, validation, pydantic, data-quality, GSSoC26

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