Awesome GitHub RepositoriesData Validation

This project is a web-based platform designed for creating, managing, and sharing professional resumes. It functions as a structured document builder that integrates artificial intelligence to assist with content generation, editing, and analysis. Users can maintain a collection of resumes, customize their visual presentation through various templates, and export them into multiple formats for job applications. The platform distinguishes itself through its autonomous AI agent capabilities, which can perform research, suggest incremental edits, and apply data patches directly to documents. It

Instructor is a schema enforcement and validation library designed to transform language model outputs into structured, type-safe data formats. It functions as a validation layer that uses Pydantic to ensure model responses conform to specific data models, acting as a tool for forcing large language models to return data in predefined schemas. The project differentiates itself through a recursive error-feedback loop that automatically retries requests when structural errors occur, passing validation failure messages back to the model to guide corrections. It also includes a streaming parser c

TypeChat is a schema enforcement library and framework for building natural language interfaces. It ensures that responses from large language models strictly adhere to predefined TypeScript type definitions, translating unstructured human language into predictable, structured data. The project functions as both a prompt generator and an output validator. It automatically creates model instructions by extracting requirements from type schemas to replace manual prompt engineering and verifies that model outputs match the required format. The system handles structured output generation and res

Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

Marshmallow is a Python data serialization library and validation framework. It functions as a bridge that transforms complex application objects into primitive data types for storage or network transmission and vice versa. The library utilizes a schema-based approach to validate that incoming data conforms to specific types and constraints. It employs a two-way transformation pipeline consisting of separate load and dump phases to handle deserialization and serialization. The framework supports class-based schema definitions with declarative field validation and recursive schema nesting for

Poml is a prompt management framework and templating engine designed for authoring, versioning, and rendering structured prompts for large language models. It uses a semantic markup language to organize prompts into reusable templates, combining them with dynamic context and data to generate formatted inputs. The system distinguishes itself by decoupling core prompt logic from final presentation through a stylesheet-based approach. It provides a dedicated JSON schema output generator to enforce strict, machine-parsable model responses and a configuration interface for managing function tool s

FLAML ist ein automatisiertes Machine-Learning-Framework, ein Tool zur Hyperparameter-Optimierung und ein Orchestrator für Large-Language-Model-Agenten. Es bietet ein System zur Modellauswahl und -abstimmung über verschiedene Lerner und Datensätze hinweg und stellt gleichzeitig ein Toolkit zur Optimierung der Inferenzparameter und Fine-Tuning-Einstellungen von Large Language Models bereit. Das Projekt verfügt über ein Meta-Learning-Tuning-System, das historische Aufgabendaten analysiert, um datenabhängige Standardkonfigurationen zu generieren und die Modellkonvergenz zu beschleunigen. Es ermöglicht zudem das Design kollaborativer Multi-Agenten-Systeme durch konversationelle Workflows und ereignisgesteuerte Orchestrierung. Die Funktionen decken eine ressourceneffiziente Hyperparametersuche für Machine-Learning-Modelle und beliebige Python-Funktionen ab und unterstützen hierarchische Suchräume sowie lexikografische Zieloptimierung. Das Framework enthält zudem Dienstprogramme für automatisierte Modellauswahl, gestapelte Ensemble-Konstruktion, Zero-Shot-Konfiguration und die Durchsetzung von Fairness-Beschränkungen. Das System unterstützt die Skalierung verteilter Abstimmungen und die gleichzeitige Ausführung von Versuchen über Compute-Cluster hinweg, um die Gesamtsuchdauer zu reduzieren.