Context-Guided Language Model Querying for Security Databases
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Solution Overview
Problem
Existing natural language processing systems struggle with accurately translating human queries into structured database queries due to a lack of context and reliance on statistical patterns, leading to ambiguity in understanding user intent.
Innovation Solution
A system that utilizes a large language model to process unstructured natural language queries by selecting and generating database queries based on similarity scores, incorporating context from technology identifiers and data schemas, thereby reducing the need for fine-tuning the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing techniques are used to translate user queries into database queries, then ease of operation is improved, but measurement precision deteriorates due to lack of context and reliance on statistical patterns
Solution Approach 1:
The system performs preliminary actions by selecting and providing relevant context information (such as schema definitions, table structures, and related data descriptions) to the language model before generating the database query. This pre-provisioning of context enables the model to accurately translate natural language queries into structured queries without requiring extensive fine-tuning, thereby maintaining both ease of operation and measurement precision.
2Manufacturing precision
If a language model is fine-tuned to improve query translation accuracy, then manufacturing precision is improved, but loss of time increases due to extensive training requirements
Solution Approach 1:
Instead of performing time-consuming fine-tuning of the language model, the system takes a preliminary action by curating and providing relevant context information (schema definitions, table structures, data descriptions) that enables the model to generate accurate database queries directly. This approach achieves manufacturing precision in query translation without incurring the time loss associated with extensive model training.
Solution Approach 2:
The system introduces an intermediary mechanism in the form of context information that mediates between the user's natural language query and the database query generation. This context acts as a bridge, providing the language model with necessary information about the database structure and semantics, thereby achieving accurate query translation without requiring the model itself to be fine-tuned, thus avoiding time loss.
3Measurement precision
If context information is provided to the language model to improve query accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The context information is segmented into distinct, manageable components such as schema definitions, table structures, column descriptions, and related data metadata. This segmentation allows the language model to process specific portions of context relevant to the query at hand, improving measurement precision while avoiding the overwhelming complexity that would result from providing all possible information at once.
Data Source
AI summary
A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.