3 Repos
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 ist eine selbst gehostete Kundensupport-Plattform, die Large Language Models und vektorisierte Wissensdatenbanken verwendet, um die Benutzerunterstützung zu automatisieren. Das System besteht aus einem einbettbaren KI-Chat-Widget für die Website-Integration, einem containerisierten Management-Dashboard und einer Vektor-Datenbank als Wissensbasis. Die Plattform enthält einen API-Orchestrator, der strukturierte API-Spezifikationen verarbeitet und es Sprachmodellen ermöglicht, mit externen Endpunkten und Operationen zu interagieren. Sie verwaltet spezialisierten Kontext für Antworten durch die Indexierung von API-Definitionen und Dokumentationen innerhalb einer Vektor-Datenbank. Die Infrastruktur deckt die containerisierte Service-Orchestrierung für Server und Hintergrund-Worker, asynchrone Queue-basierte Verarbeitung sowie ein administratives Interface zur Überwachung von Echtzeit-Benutzerinteraktionen ab. Das System handhabt zudem Datenbank-Schema-Migrationen und Wissensindexierung.
Injects structured API definitions as context into the language model to enable automated interaction with external operations.