Context Orchestration Engine for Multi-Order Text Queries
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Solution Overview
Problem
Existing digital assistant systems are inflexible and inaccurate in responding to higher-order text queries, as they are rigidly designed for single-order queries and cannot adapt to multifaceted responses across multiple tasks and data sources.
Innovation Solution
A multi-order query result system utilizing a context orchestration engine and large language model to break down higher-order text queries into context-defining subcomponents, generating domain-specific computer code to access various data sources and combine results for accurate and comprehensive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing digital assistant systems use rigid single-order query processing, then system simplicity is maintained, but adaptability to higher-order queries deteriorates
Solution Approach 1:
The system segments higher-order text queries into multiple context-defining query subcomponents, each targeting specific data sources. This decomposition allows the system to handle complex multifaceted queries by processing them as a series of simpler, manageable sub-queries that can be executed independently and then aggregated.
Solution Approach 2:
The context orchestration engine serves multiple functions: it analyzes queries, identifies subcomponents, determines data sources, generates executable code, and aggregates results. This multi-functional design enables a single system to handle both single-order and higher-order queries without requiring separate specialized systems.
2Measurement precision
If existing systems are fixed for single-order queries, then processing simplicity is maintained, but response accuracy for higher-order queries deteriorates
Solution Approach 1:
By breaking down higher-order queries into discrete context-defining subcomponents, each with specific data source requirements, the system can accurately target and retrieve information from the correct sources. This segmentation prevents inaccurate responses by ensuring each sub-query is precisely answered before aggregation.
Solution Approach 2:
The system incorporates feedback mechanisms where the context orchestration engine analyzes the original query, identifies necessary subcomponents and data sources, then uses the results from executing those subqueries to generate the final accurate response. This iterative feedback loop ensures accuracy even for complex higher-order queries.
3Adaptability or versatility
If systems generate responses for single-order queries only, then system simplicity is maintained, but flexibility in handling multifaceted queries deteriorates
Solution Approach 1:
The system dynamically adapts its processing approach based on the query type. For single-order queries, it uses simple direct processing, while for higher-order queries, it automatically activates the context orchestration engine to decompose and handle them as multifaceted problems requiring multiple data sources and code execution.
Solution Approach 2:
The context orchestration engine acts as an intermediary layer between the user's high-level text query and the execution of specific code against data sources. This intermediary component provides the flexibility to handle multifaceted queries by translating them into appropriate subcomponents and coordinating the necessary operations.
4Adaptability or versatility
If existing systems use fixed single-source processing, then implementation simplicity is maintained, but ability to access multiple data sources deteriorates
Solution Approach 1:
The system segments data source access by creating separate context-defining query subcomponents for each required data source. This allows the system to access multiple data sources independently through generated code, with each subcomponent specifying the exact data source and access method needed, simplifying the overall implementation of multi-source access.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating responses to multi-order text queries using a context orchestration engine. For example, the disclosed systems generate context-defining query subcomponents from a multi-order text query, where the context-defining query subcomponents indicate contextual data sources pertaining to their respective portions of the multi-order text query. In addition, the disclosed systems provide or transmit the context-defining query subcomponents to a large language model for domain-specific computer code pertaining to each respective context-defining query subcomponent. The disclosed systems can further execute the generated computer code for each context-defining query subcomponent to access indicated contextual data sources for generating component-specific results. The disclosed systems can also generate a multi-order result to the multi-order text query from the component-specific results.