Natural-Language Query Routing With Native Database Commands
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Solution Overview
Problem
Large language models (LLMs) are unpredictable and resource-intensive, making them unsuitable for precise, time-sensitive business applications due to varying responses, conflicting information, and security concerns, and they require significant user involvement and time to generate answers.
Innovation Solution
A database query processing service efficiently processes queries by using native database commands for non-natural language queries and leveraging LLMs only when necessary, generating natively valid replacement content and ensuring data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to generate responses to natural language prompts, then the system can handle unbounded natural language possibilities and provide creative responses, but the processing time increases to seconds or minutes and resource consumption becomes excessive for high-throughput environments
Solution Approach 1:
The patent segments the query processing workload into two distinct paths: a fast path for structured queries that can be handled by traditional database systems, and a slow path for unstructured natural language queries that require LLM processing. This segmentation allows the system to apply the appropriate processing method to each query type, maximizing throughput for structured queries while maintaining natural language处理能力.
Solution Approach 2:
The patent introduces an intermediary component that detects whether incoming queries contain natural language markers and routes them appropriately. This intermediary acts as a gateway between the user and the two processing paths, determining whether LLM resources should be allocated based on the query characteristics, thereby optimizing resource utilization.
2Reliability
If LLMs leverage extensive information and multiple neural network layers to generate responses, then the system can provide comprehensive answers, but the processing time extends to seconds or minutes which is unacceptable for time-sensitive business applications
Solution Approach 1:
The patent divides query processing into two separate pathways: a fast path for structured queries handled by traditional database systems that can return results immediately, and a slow path for natural language queries that require LLM processing. This segmentation ensures that time-sensitive structured queries are resolved quickly while natural language queries receive comprehensive LLM-based answers.
Solution Approach 2:
The patent implements preliminary detection of natural language markers in incoming queries before routing them to the appropriate processing path. This preliminary action allows the system to prepare and execute the fast path for structured queries without delay, while only engaging the resource-intensive LLM pathway when actually necessary.
3Reliability
If LLMs are configured to provide precise and consistent responses for business contexts, then the system can deliver predictable answers, but the resource consumption and processing time remain excessively high
Solution Approach 1:
The patent segments the processing workload to apply LLM resources only to queries that genuinely require natural language understanding, while structured queries are handled efficiently by traditional database systems. This segmentation dramatically reduces overall resource consumption while maintaining response predictability for both query types through appropriate processing methods.
Solution Approach 2:
The patent introduces an intermediary detection mechanism that identifies whether queries require LLM processing based on the presence of natural language markers. This intermediary optimizes resource allocation by preventing unnecessary LLM engagement for structured queries, thereby reducing computational overhead while maintaining response quality.
4Adaptability or versatility
If LLMs are made accessible to multiple users for natural language queries, then the system provides versatile natural language processing, but security boundaries may be violated as LLMs may use private data from one user to answer questions for another user
Solution Approach 1:
The patent segments the query processing into isolated pathways where structured queries with clear security boundaries are handled by traditional database systems that enforce access controls at the data level. This segmentation protects sensitive data from LLM-based security violations while maintaining multi-user natural language processing capabilities through the slow path.
Solution Approach 2:
The patent introduces an intermediary layer that detects and routes queries appropriately, preventing direct access to LLM resources for queries that should be handled by the secure fast path. This intermediary acts as a security gateway that maintains data protection boundaries while enabling versatile natural language processing where appropriate.
Data Source
AI summary
A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.