7 Repos
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.
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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.
Dieses Tool fungiert als Model Context Protocol Server, der KI-Modelle mit lokalen Entwicklungsumgebungen verbindet. Es ermöglicht KI-Assistenten die Analyse von Codebases, die Ausführung von CLI-Utilities und automatisierte Code-Anpassungen direkt in lokalen Projektdateien. Durch die Integration der Gemini API ermöglicht das System eine tiefe Interaktion zwischen externen Modellen und lokalen Systemressourcen. Das Projekt zeichnet sich durch ein robustes Sicherheits- und Zuverlässigkeitskonzept für automatisierte Workflows aus. Es erzwingt strikte pfadbasierte Zugriffskontrollen zum Schutz sensibler Dateien und nutzt isolierte Sandbox-Umgebungen für die Ausführung generierten Codes. Um einen kontinuierlichen Betrieb zu gewährleisten, implementiert das Tool ein dynamisches Fallback-Routing für Modelle, das bei Erreichen von Nutzungslimits automatisch zwischen Modell-Tiers wechselt, und nutzt sekundäre Modell-Validierung zur Prüfung der Ausgabequalität. Das System unterstützt eine breite Palette technischer Operationen, darunter die strukturierte Datenextraktion aus Terminal-Ausgaben, die Verwaltung der Konversationshistorie und die Konfiguration von Ausführungsparametern für langlaufende Aufgaben. Es bietet umfassende Funktionen zum Scannen von Projektverzeichnissen, zur Generierung technischer Insights und zur Verwaltung von Kontextfenstern für umfangreiche Dokumentationen und Codebases.
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.