Modular LLM Workflow Integration With Shared Memory Bridge
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Solution Overview
Problem
Developing and deploying artificial intelligence solutions requires significant memory, time, and specialized expertise, hindering widespread adoption and efficient integration.
Innovation Solution
A system utilizing a configuration server with a processing element, large language models, and a shared memory with a language bridge to facilitate drag-and-drop visual programming, enabling efficient deployment and integration of customizable functionality modules across different operational environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial intelligence solutions are developed and deployed using traditional methods, then functionality and capabilities are improved, but memory consumption and development time increase significantly
Solution Approach 1:
The system segments AI functionality into modular, reusable components that can be independently developed and deployed. These modular AI solutions can be selectively integrated into different systems, reducing the memory footprint required for each individual implementation while maintaining comprehensive AI capabilities across the ecosystem.
Solution Approach 2:
The patent creates universal AI modules that can serve multiple functions and be deployed across different systems and operational environments. These multi-functional modules eliminate the need for duplicating AI capabilities in each system, thereby reducing overall memory consumption while expanding adaptability.
2Adaptability or versatility
If artificial intelligence solutions are developed using traditional methods, then capabilities are improved, but development time and expertise requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-developing, testing, and validating AI modules in advance. These pre-configured modules can be directly deployed into different operational environments without requiring extensive re-development, thereby significantly reducing development time while maintaining advanced AI capabilities.
Solution Approach 2:
The patent enables copying of validated AI modules across different systems and environments. Once an AI module is developed and tested in one environment, it can be replicated and deployed elsewhere with minimal modification, reducing development time and the need for specialized expertise in each deployment scenario.
3Adaptability or versatility
If traditional AI integration methods are used, then functionality is achieved, but system complexity and expertise requirements increase
Solution Approach 1:
The system introduces an intermediary layer that standardizes the interface between AI modules and operational environments. This intermediary abstraction layer simplifies integration by handling environment-specific complexities internally, allowing AI modules to be deployed across different environments without increasing system complexity or requiring specialized expertise.
Data Source
AI summary
A system for creating functionality modules for deployment in a workflow for use in visual programming including a configuration server with a processing element operable to implement the functionality modules and workflow, at least one large language model, a customizable functionality module in a workflow including at least one interface defining one or more customizable properties, and wherein the workflow executes a first operational environment different from a second operational environment executed by the large language models.