3 repositorios
Injection of relevant background information into the prompt prior to query processing.
Distinct from On-Demand Context Loading: Distinct from on-demand loading: focuses on the proactive injection of context to ensure informed initial responses.
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This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin
Injects relevant background information into the model before processing a query to ensure informed responses.
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Pre-loads heavy resources like models and lookup tables at server startup to reduce per-request overhead.
Copilot es una plataforma de soporte al cliente autohospedada que utiliza modelos de lenguaje grandes (LLM) y bases de conocimiento vectorizadas para automatizar la asistencia al usuario. El sistema consiste en un widget de chat de IA incrustable para integración en sitios web, un panel de gestión contenedorizado y una base de conocimiento de base de datos vectorial. La plataforma incluye un orquestador de API que procesa especificaciones de API estructuradas, permitiendo que los modelos de lenguaje interactúen con endpoints y operaciones externas. Gestiona contexto especializado para las respuestas indexando definiciones de API y documentación dentro de una base de datos vectorial. La infraestructura cubre la orquestación de servicios contenedorizados para servidores y trabajadores en segundo plano, procesamiento asíncrono basado en colas y una interfaz administrativa para monitorear las interacciones de los usuarios en tiempo real. El sistema también maneja migraciones de esquemas de bases de datos e indexación de conocimiento.
Injects structured API definitions as context into the language model to enable automated interaction with external operations.