Log Relevancy Sieve Using Tree Graphs for LLM Context Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current language models face challenges in analyzing large and complex log files due to context window limitations, inefficient preprocessing techniques, and the need for high-quality data, leading to impractical manual analysis, extended downtime, high resource demands, and incomplete data analysis.
Innovation Solution
An unsupervised relevancy sieve is employed to filter log data by constructing a directed root tree graph, refining messages based on predefined relationships, and selecting representative messages to generate a relevancy-filtered file for language model prompts, ensuring relevant information is retained without requiring supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language models are used to analyze log files, then analysis accuracy is improved, but context window limitations prevent processing of entire large log files
Solution Approach 1:
The patent extracts only the most relevant log messages from the entire log file using an unsupervised relevancy sieve that identifies and selects representative messages based on anomaly detection and clustering algorithms, thereby reducing the input size to fit within language model context windows while preserving critical information for accurate analysis
Solution Approach 2:
The patent segments the large log file into multiple smaller chunks or batches that can be processed individually by the language model, using automated sampling and relevancy filtering to ensure each segment contains representative data that maintains overall analysis accuracy
2Productivity
If traditional filtering methods are used to reduce log file size, then processing efficiency is improved, but relevant information is lost
Solution Approach 1:
The patent employs an unsupervised relevancy sieve that automatically identifies and retains relevant log messages without human intervention or predefined filtering rules, using anomaly detection and clustering algorithms to self-determine which messages contain critical information, thereby maintaining both processing efficiency and information completeness
Solution Approach 2:
The patent changes the filtering parameters from traditional anomaly-based filtering to relevancy-based filtering that considers multiple dimensions including message frequency, temporal patterns, and contextual relationships, allowing retention of relevant information while achieving efficient processing through automated sampling strategies
3Loss of information
If manual log analysis is performed, then comprehensive understanding is achieved, but time consumption and resource demands increase significantly
Solution Approach 1:
The patent performs preliminary automated processing of log files including relevancy filtering, anomaly detection, and representative message selection before language model analysis, thereby preparing the data in advance to reduce the time required for actual analysis while maintaining comprehensive understanding through preserved critical information
Solution Approach 2:
The patent introduces an intermediary unsupervised relevancy sieve system between the raw log files and the language model, which automatically processes and filters the data to extract relevant information, thereby reducing the time burden on manual analysts while preserving comprehensive analysis capabilities through automated sampling and anomaly detection
4Measurement precision
If entire log files are processed by language models, then complete analysis is achieved, but resource requirements become prohibitively high
Solution Approach 1:
The patent extracts only the most relevant and representative log messages using an unsupervised relevancy sieve that applies anomaly detection and clustering algorithms to identify critical patterns, thereby reducing the computational input size while maintaining analysis completeness through selective retention of information-rich messages
Solution Approach 2:
The patent applies partial processing by analyzing a carefully selected subset of log messages that are deemed most representative and relevant, using automated sampling strategies that process fewer messages than the complete file while achieving comparable analysis completeness at reduced computational cost
Data Source
AI summary
In one implementation, a device may generate cleaned log messages by removing irrelevant data from log messages. The device may construct a directed root tree graph for the cleaned log messages. The device may refine the cleaned log messages in the directed root tree graph based on predefined relationships established in the directed root tree graph. The device may select representative messages from the cleaned log messages in the directed root tree graph to generate a relevancy-filtered file configured for inclusion in a language model prompt.