7 repositorios
Settings for managing concurrency, retry logic, and performance tuning during evaluation tasks.
Distinct from Model Parameter Configurations: Distinct from Model Parameter Configurations: focuses on evaluation-specific execution parameters like concurrency and retries rather than model fine-tuning.
Explore 7 awesome GitHub repositories matching artificial intelligence & ml · Execution Parameter Configurators. Refine with filters or upvote what's useful.
Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t
Enables configuration of task parameters via command line or external files to override default behaviors.
Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin
Adjusts concurrency and retry logic for evaluation tasks to manage performance and reliability.
zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ
Sets global logging and concurrency levels during startup to optimize runtime performance and observability.
Empire is a post-exploitation command-and-control (C2) framework designed for red team operations. It deploys and manages agents written in PowerShell, Python, C#, Go, and C across Windows, Linux, and macOS, using encrypted communication channels over HTTP, HTTPS, and SMB. The framework executes over 400 built-in modules for reconnaissance, privilege escalation, credential theft, and lateral movement, and provides a modular engine for authoring custom attack modules. What sets Empire apart is its multi-language agent deployment system, which allows operators to choose implants that suit each
Configures module parameters, selects language, reviews security notes, and chooses execution mode.
Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di
Adjusts system-level settings to optimize performance for specific workloads.
Esta herramienta funciona como un servidor de Model Context Protocol que conecta modelos de inteligencia artificial con entornos de desarrollo locales. Permite a los asistentes de IA realizar análisis de código, ejecutar utilidades de línea de comandos y aplicar modificaciones automatizadas directamente en los archivos del proyecto local. Al integrarse con la API de Gemini, el sistema facilita una interacción profunda entre modelos externos y los recursos del sistema local. El proyecto destaca por un marco robusto de seguridad y fiabilidad diseñado para flujos de trabajo de desarrollo automatizados. Aplica controles de acceso estrictos basados en rutas para proteger archivos sensibles y utiliza entornos sandbox aislados para ejecutar el código generado. Para asegurar una operación continua, la herramienta implementa un enrutamiento dinámico de fallback de modelos, que cambia automáticamente entre niveles de modelos si se alcanzan los límites de uso, y emplea un modelo secundario de evaluación para validar la calidad de las salidas generadas. El sistema admite una amplia gama de operaciones técnicas, incluyendo la extracción de datos estructurados desde la salida de la terminal, la gestión del historial de conversaciones y la configuración de parámetros de ejecución para tareas de larga duración. Proporciona capacidades integrales para escanear directorios de proyectos, generar insights técnicos y gestionar ventanas de contexto para manejar documentación extensa e información del código fuente.
Provides configurable runtime settings for timeouts and output handling to support long-running AI development tasks.
This project serves as a curated directory and resource hub for developers working with generative artificial intelligence. It provides a comprehensive index of open-source software solutions, frameworks, and project examples designed to help users discover and implement advanced AI systems. The repository focuses on practical implementations of agentic, multimodal, and retrieval-augmented generation architectures. It highlights tools for building conversational assistants, voice-enabled agents, and automated workflows that leverage large language models. By showcasing diverse technical domai
Manages concurrency, retry logic, and performance tuning during evaluation tasks.