Language Model Query Context for Accurate Security Database Search
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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 interpreting queries like 'what is jay?', which could refer to the letter 'J' or the name 'Jay' or a bird species.
Innovation Solution
A system that receives an unstructured natural language query, selects a group of database queries based on similarity, generates a context using a language model, and executes a refined database query on a security database, utilizing techniques like vectorization, similarity scoring, and re-ranking to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing techniques are used to translate human 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 retrieving relevant context data from the database before generating the final query. This includes fetching entity information, relationship data, and attribute details that provide contextual grounding for the language model, thereby improving measurement precision while maintaining ease of operation.
Solution Approach 2:
The system introduces an intermediary context retrieval mechanism between the natural language query and the database query generation. This intermediary layer gathers relevant contextual information from the database and presents it to the language model, acting as a mediator that bridges the gap between unstructured language and structured data while improving accuracy.
2Measurement precision
If context is added to language model processing by retrieving relevant data from the database, then measurement precision is improved, but loss of time increases due to additional data retrieval and processing steps
Solution Approach 1:
The system applies partial action by selectively retrieving only the most relevant context data needed for query generation, rather than fetching all available data. This involves filtering and prioritizing context based on the specific query requirements, thereby improving measurement precision while minimizing the time loss associated with data retrieval.
3Reliability
If the system retrieves and processes additional context data before query generation, then reliability is improved, but device complexity increases due to additional components and processing steps
Solution Approach 1:
The system applies universality by designing a multi-functional context retrieval and processing mechanism that serves multiple purposes: it retrieves relevant data, filters and prioritizes information, and prepares contextualized input for the language model. This unified approach improves reliability while managing device complexity by consolidating functions into a single integrated process.
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.