Distributed Orchestration for Generative AI Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently managing and retrieving relevant data from diverse sources to perform natural language tasks, leading to issues like data fragmentation, resource costs, and the need for custom data retrieval solutions.
Innovation Solution
The implementation of distributed orchestration of natural language tasks using a generative machine learning model, which allows for customized retrieval from various resources, optimizes data usage, and improves the performance of generative machine learning systems by coordinating data retrieval and accessing relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored across many different systems or services, then information capacity and storage flexibility are improved, but data retrieval complexity and time increase
Solution Approach 1:
The system segments data retrieval operations by maintaining separate data repositories for different data types (e.g., knowledge base, codebase, documentation) and uses specialized retrievers for each repository. This allows parallel retrieval operations across multiple data sources simultaneously, reducing overall retrieval time while maintaining the benefits of distributed storage.
Solution Approach 2:
The patent introduces a retrieval configuration and orchestration layer that acts as an intermediary between natural language queries and multiple data repositories. This intermediary coordinates retrieval operations across different systems, manages context windows, and synthesizes results, thereby reducing the time penalty of accessing distributed data.
2Measurement precision
If custom data retrieval solutions are implemented for each system, then data access precision is improved, but system complexity and expertise requirements increase
Solution Approach 1:
The patent implements a universal retrieval configuration system that can access multiple different data repositories (knowledge bases, codebases, documentation systems) through a unified interface. The same orchestration framework handles diverse data types and retrieval patterns, eliminating the need for separate custom retrieval solutions for each system while maintaining precise data access.
Solution Approach 2:
The system uses parameterized retrieval configurations where data access precision is controlled through configurable parameters (e.g., context window size, retrieval thresholds, filtering criteria) rather than custom-coded solutions. This allows precise data retrieval across different repositories by adjusting parameters rather than implementing complex custom logic.
3Adaptability or versatility
If generative machine learning models are used for natural language tasks, then task versatility is improved, but resource consumption and costs increase
Solution Approach 1:
The patent applies partial retrieval and selective prompting to generative models, where only the most relevant data portions are retrieved and included in the context window rather than providing all available data. This reduces the computational resources required for processing while maintaining high task versatility through the generative model's ability to handle diverse natural language queries.
Solution Approach 2:
The system performs preliminary data retrieval and filtering before generating responses, pre-organizing data into context windows and caching frequently accessed information. This preliminary action reduces the computational burden on generative models during the actual generation process, lowering resource consumption while preserving the model's versatility for various natural language tasks.
Data Source
AI summary
Distributed orchestration of data retrieval for generative machine learning model may be performed. When a natural language request to perform a natural language task is received that is associated with a generative application, one or more data retrievers may be selected to access associated data repositories according to a previously specified retrieval configuration for the generative natural language application. The data may then be obtained by the selected data retrievers and used to generate a prompt to a generative machine learning model. A result of the generative machine learning model may then be used to provide a response to the natural language request to perform the natural language task.