ragalgo-v3

ragalgo-v3 utilizes a Model Context Protocol (MCP) to manage and orchestrate multiple AI models based on user-defined contexts. This allows for dynamic switching between models depending on the specific task or input, leveraging a centralized context manager that tracks state and context across requests. The architecture is designed to minimize latency by maintaining context in memory, enabling rapid context switching without the need for repeated initialization of models.

This capability allows ragalgo-v3 to maintain and update context dynamically as user interactions occur. It employs a context stack that records previous interactions and their outcomes, enabling the system to provide more relevant responses based on historical data. This is achieved through a combination of in-memory storage and efficient retrieval algorithms that prioritize recent context for quick access.

ragalgo-v3 supports integration with various AI models through a standardized API interface, allowing developers to plug in different models without changing the core application logic. This is facilitated by a modular architecture that abstracts the model interaction layer, enabling easy addition or removal of models as needed. The system also provides a set of predefined adapters for popular models, streamlining the integration process.