Projects sharing features with Struct

Zod is a TypeScript-first schema declaration and validation library designed to ensure end-to-end data integrity. It functions as a runtime type guard, allowing developers to define complex data structures through a declarative, chainable syntax. By using these schema definitions, the library automatically derives static TypeScript types, eliminating the need for manual type duplication and ensuring that runtime data matches expected application contracts. The library distinguishes itself through functional schema composition, which enables the creation of hierarchical structures by nesting a

Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality suite and performance evaluator designed to verify the integrity and performance of models and datasets from research through production. The project functions as a model monitoring tool for tracking data drift and performance degradation in production environments. It allows for the creation of custom validation suites and utilizes a pluggable check architecture to automate quality checks within continuous integration pipelines. The framework covers a broad range of capabil

Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of

Modeling domain data on the basis of Clojure records.

Efficient, hassle-free function call validation with a concise inline syntax for clojure.spec and Malli

Superstruct is a JavaScript and TypeScript data validation library used to verify that data structures match defined shapes and types. It functions as a composable schema builder and a TypeScript schema validator, ensuring that runtime data checks remain synchronized with static type definitions. The library features a data coercion engine that transforms input values or injects default values before the validation process is executed. It enables the creation of complex validation rules by nesting, merging, or omitting properties from existing structures. Its capabilities cover the validatio

Complete instrumentation for clojure.spec

Yup is a JavaScript schema validation library used to define data shapes and validate runtime values. It functions as an object schema validator and a data coercion engine, allowing developers to transform raw input values into desired types before performing validation checks. The library is distinguished by its support for dynamic schema validation, where rules can be adjusted at runtime based on sibling field values or external context. It also enables recursive data structuring for polymorphic fields and provides a system for extracting static TypeScript interfaces from runtime schema def

This project is a Python implementation of the JSON Schema specification, providing a library for verifying that data instances conform to defined schemas. It serves as a data validation framework capable of validating both the data itself and the schemas against official meta-schemas to ensure structural correctness. The library features a schema reference resolver that maps URIs to definitions, enabling the resolution of internal and remote references for modular schema management. It is designed for extensibility, allowing for the definition of custom keywords, custom type-checking logic,

Schema validation just got Pythonic

A validation DSL for Clojure & Clojurescript applications

A JSONSchema validator that uses code generation to be extremely fast

A simple validation library for Clojure and ClojureScript

High-performance data-driven data specification library for Clojure/Script.

:whitecheckmark: Easy property validation for JavaScript, Node and Express.

Clojure(Script) library for declarative data description and validation

Lightweight data validation and adaptation Python library.

Pydantic is a data validation and serialization library that enforces schema constraints and performs type conversion on complex data structures. It utilizes standard Python type annotations to define data models, allowing developers to establish structured schemas that automatically enforce business rules and constraints without the need for custom domain-specific languages. The library distinguishes itself by transforming high-level model definitions into optimized code during initialization to minimize runtime overhead. It supports recursive validation for nested data structures and employ

Lightweight, extensible data validation library for Python

This project is a JSON Schema validation library and framework used to verify that data instances conform to declarative definitions. It functions as a validation engine that enforces structural constraints and data types, while also serving as a meta-validator to ensure schema definitions themselves are syntactically correct against official meta-schemas. The library is designed for extensibility, allowing users to define custom validation logic by mapping schema keywords to specialized callable functions. It includes a registry-based reference resolver for managing internal and external URI

Schema-Inspector is a simple JavaScript object sanitization and validation module.

Python Data Structures for Humans™.

Joi is a JavaScript data validation library used to define schemas that ensure the structure and data types of objects remain consistent. It functions as a schema-based validator and object schema definition tool, preventing invalid information from entering an application by checking data against predefined constraints and rules. The library employs a chainable fluent interface and a constraint-based validation engine to build complex validation pipelines. It utilizes recursive tree traversal to validate nested data structures and a type-coercion pipeline to transform input values into the t

Library for exploring and validating machine learning data

Pandera is a data pipeline validation framework and statistical type validation tool. It functions as a library for defining and enforcing schemas on datasets to ensure data quality and consistency, specifically providing validation capabilities for Pandas dataframes. The project includes a schema inference tool that automates setup by analyzing existing dataset samples to generate validation schemas. It also serves as a synthetic data generator, creating artificial datasets based on predefined schemas to verify data-producing functions. The framework covers data engineering quality assuranc