keris_edumcp

This capability allows for seamless integration with various AI models through a Model Context Protocol (MCP) server. It utilizes a modular architecture that supports multiple model endpoints, enabling dynamic context switching and efficient resource management. The server acts as a middleware, orchestrating requests and responses between clients and AI models, ensuring that context is preserved across interactions.

This capability enables the server to switch contexts dynamically based on user inputs or session data. By maintaining a stateful interaction model, it can adapt to different user needs and maintain continuity in conversations or tasks. This is achieved through a session management system that tracks user interactions and context history.

This capability allows the server to handle requests to multiple AI models simultaneously, optimizing resource usage and response times. It employs an asynchronous request handling mechanism that queues requests and distributes them to the appropriate model based on predefined rules or user preferences.

This capability provides a customizable routing mechanism that allows developers to define rules for directing requests to specific AI models based on input parameters. It uses a rule-based engine that evaluates incoming requests and determines the appropriate model to handle each one, enhancing flexibility in model usage.

This capability allows the server to manage user sessions effectively, ensuring that context is preserved across multiple interactions. It utilizes a session store that keeps track of user-specific data and interactions, enabling personalized experiences and continuity in conversations.