3 dépôts
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 est une plateforme de support client auto-hébergée qui utilise des grands modèles de langage (LLM) et des bases de connaissances vectorisées pour automatiser l'assistance utilisateur. Le système se compose d'un widget de chat IA intégrable pour le web, d'un tableau de bord de gestion conteneurisé et d'une base de connaissances en base de données vectorielle. La plateforme inclut un orchestrateur d'API qui traite des spécifications d'API structurées, permettant aux modèles de langage d'interagir avec des points de terminaison et des opérations externes. Il gère un contexte spécialisé pour les réponses en indexant les définitions d'API et la documentation au sein d'une base de données vectorielle. L'infrastructure couvre l'orchestration de services conteneurisés pour les serveurs et les travailleurs en arrière-plan, le traitement asynchrone basé sur des files d'attente, et une interface administrative pour surveiller les interactions utilisateur en temps réel. Le système gère également les migrations de schéma de base de données et l'indexation des connaissances.
Injects structured API definitions as context into the language model to enable automated interaction with external operations.