Natural Language Query Embedding Filter for Invalid Query Screening
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Solution Overview
Problem
Conventional database management systems (DBMS) inefficiently utilize computationally intensive machine learning models to convert natural language queries into structured queries, even when the output is likely to be invalid, wasting resources and computational power.
Innovation Solution
Implement a system that filters natural language queries by encoding them into query embeddings and comparing to valid embeddings to determine validity, avoiding the use of machine learning models for invalid queries, thus saving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used to convert all natural language queries into structured queries, then query conversion capability is improved, but computational resource waste increases
Solution Approach 1:
The system performs preliminary filtering of natural language queries using embedding similarity comparison before submitting them to the computationally intensive machine learning model. By pre-identifying queries that are likely to produce valid structured queries through embedding space proximity, the system avoids unnecessary computational resource consumption on queries that would inevitably fail conversion, while maintaining the ability to convert valid queries successfully.
2Ease of operation
If machine learning models convert natural language queries, then natural language query processing is enabled, but computational cost increases
Solution Approach 1:
The query processing system is segmented into two distinct stages: a lightweight filtering stage using embedding similarity comparison and a heavy machine learning conversion stage. This segmentation allows the system to quickly filter out invalid queries at low computational cost, reserving the expensive machine learning model only for queries with high probability of successful conversion, thereby reducing overall computational cost while maintaining natural language query processing capability.
3Productivity
If all natural language queries are processed through machine learning conversion, then query conversion completeness is improved, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary filtering using embedding similarity comparison to identify queries likely to succeed before submitting them to the machine learning model. This preliminary action maintains conversion completeness for valid queries while dramatically improving processing efficiency by avoiding time-consuming machine learning inference on queries that would fail anyway, thus reducing overall processing time without sacrificing conversion completeness for valid queries.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for data processing include receiving a natural language query including a request for data from a database, generating a natural language query embedding representing the natural language query in a vector space, and determining a validity of the natural language query by comparing the natural language query embedding to a valid query embedding in the vector space. Some embodiments include converting the natural language query into a structured query based on the validity of the natural language query and retrieving the data from the database using the structured query.