🚝LLM Interoperability Layer

LLM Sharing Protocol

The LLM sharing protocol includes several key components, collectively building a robust and flexible communication framework.

• Data Format Standardization: Adopting unified data structures (JSON and Protocol Buffers) establishes a standard foundation for data exchange between models. The system uses a unified JSON request format, including clearly defined request types, parameter configurations, and contextual information, ensuring consistency in data interactions.

• Communication Interface Specification: Establishes standardized API structures, integrating RESTful API and gRPC request methods, equipped with complete endpoint definitions and security authentication mechanisms, implementing standardized model calling processes.

• Version Control Mechanism: Adopts systematic protocol version management, ensuring seamless collaboration between different protocol versions through automated version incrementation and backward compatibility guarantees. This multi-layered architectural design significantly enhances system communication efficiency, scalability, and maintainability.

LLM Universal Environment

The LLM universal environment provides a unified and efficient runtime and development environment, specifically designed for Large Language Models (LLMs) with large parameter counts and model sizes. This environment integrates Ollama technology, not only simplifying LLM deployment and management processes but also optimizing resource utilization to ensure high performance and scalability.

• Containerization Support: The system employs container technologies like Docker and Kubernetes, encapsulating various LLMs in independent containers to ensure environment consistency and portability. Ollama provides optimized large model container images with built-in necessary dependencies and configurations, supporting rapid deployment and elastic scaling. This containerization approach makes LLM deployment more modular and facilitates cross-environment migration and management.

• GPU Optimization Scheduling: Ollama integrates intelligent scheduling algorithms that can dynamically allocate GPU resources based on model demands and resource conditions, maximizing computational efficiency. For example, the system automatically allocates more GPU instances during peak periods to meet concurrent request demands.

• Memory and Storage Optimization: The system employs distributed storage and memory management technologies to ensure efficient loading and access of large model data. Through compression techniques and memory paging mechanisms, it effectively reduces memory usage and improves data transfer speeds.

• Automatic Scaling: Based on Kubernetes' auto-scaling functionality, the universal environment can automatically adjust the number of model instances according to real-time load, maximizing resource utilization while effectively controlling operational costs.

Workflow Graphical Editor

The workflow graphical editor is an integrated visual tool specifically designed to simplify the design, configuration, and management of complex Large Language Model (LLM) workflows. By integrating advanced tools like AutoGen and LangGraph, this editor not only optimizes user experience but also significantly enhances workflow flexibility and scalability. Users only need to drag and drop predefined nodes (including input, processing, output nodes, etc.) onto the canvas to intuitively build workflows. Each node represents specific operations or steps and supports various LLM tasks such as text generation, translation, and sentiment analysis.

Agent Optimal Path Module

The Agent optimal path module builds an intelligent and efficient task optimization system by integrating key components such as natural language interpreters, LLM planners, reflection and improvement, memory-enhanced planning, collaborative training, and evaluation. These components work together to ensure the system can accurately understand user requirements, formulate optimal execution plans, and continuously improve through feedback and optimization. This content will be detailed and analyzed in Section ~\ref{agent_optimal_path}.