Log Data Embedding Structure for Faster, More Relevant AI Responses
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Solution Overview
Problem
Existing systems struggle to efficiently process and analyze large volumes of log files generated from multiple devices across distributed data centers due to format differences and the inability of generative AI applications to quickly parse and understand log content, leading to challenges in generating targeted and relevant responses.
Innovation Solution
A system that generates metadata for log files, converting them into structured data objects with vector embeddings that include positional and temporal information, allowing for improved processing and analysis by generative AI applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If log files are collected from multiple distributed devices with different formats, then the volume of log data increases, but the complexity of processing and analysis increases
Solution Approach 1:
The patent segments log files into fixed-size chunks with overlapping portions, creating manageable units that can be processed independently. Each chunk is assigned a unique identifier and metadata, allowing the system to handle large volumes of log data without being overwhelmed by the total size. This segmentation transforms the monolithic processing task into many smaller, parallelizable operations.
Solution Approach 2:
The patent introduces an intermediary processing layer that standardizes log data from multiple devices with different formats. This layer parses various log formats, extracts relevant information, and converts them into a unified structure with consistent metadata fields. This intermediary transformation eliminates the need to handle each device's unique format separately, reducing processing complexity.
2Measurement precision
If generative AI applications attempt to parse and understand log content directly, then response relevance may be achieved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary processing of log files by pre-parsing, chunking, and extracting metadata before the generative AI application needs to analyze the content. This advance preparation creates ready-to-use data structures with embedded contextual information, allowing the AI application to quickly retrieve and understand relevant log entries without performing time-consuming parsing operations during query processing.
Solution Approach 2:
The patent extracts key information from log files and separates it into structured metadata fields and contextual data. By pulling out essential elements such as timestamps, device identifiers, error codes, and operational parameters into dedicated fields, the system enables the generative AI application to quickly scan and understand log content without processing the entire raw text, thus improving both speed and relevance.
3Productivity
If log files are standardized to a single format, then processing efficiency improves, but adaptability to handle diverse device formats decreases
Solution Approach 1:
The patent creates a universal log data structure that can accommodate multiple device formats through flexible metadata fields. The standardized structure includes generic fields that can capture information from any device type, while also allowing device-specific extensions. This universal framework enables the system to process logs from diverse sources efficiently without sacrificing the ability to adapt to new or specialized formats.
Solution Approach 2:
The patent applies local quality by allowing different portions of the standardized log structure to have different levels of detail based on the source device. Common fields are standardized for efficient processing, while device-specific fields retain their original format characteristics. This approach enables bulk processing efficiency for shared operations while preserving local adaptability for device-specific requirements.
Data Source
AI summary
Systems and methods are provided for generating metadata for an improved data structure that enables improvements to generative AI and LLM processing and use of the data. A system may receive a log file generated by an entity associated with a device and generate metadata of the log file that comprises origination data associated with the entity and location of the device. The system may store the metadata and content of the log file as a document object. A data structure may be generated using various data objects. For example, the system can generate a vector embedding object enriched with a temporal embedding, a positional embedding, or a hierarchy of the metadata. The system may initiate an action based on the vector embedding object.