Self-Evolutionary Group-wise Log Parsing Based on Large Language Model

In this paper we propose self-evolving method called SelfLog，which, on one hand, uses similar pairs extracted by LLM itself in the historical data to act as the prompt of a new log, allowing the model to learn in a self-evolution and labeling-free way. On the other hand, we propose an N-Gram-based grouper and log hitter.

Repository Organization

├── evaluate/ # │ ├── evaluator/ # the evaluation code of GA, PA, PTA, RTA │ └── evaluator_PA/ # calculate PA, PTA, RTA result ├── functions/ # mian part of SelfLog │ ├── benchmark_settings/ # log data process │ ├── gram/ # N-gram based grouper │ ├── llm_func/ # requst llm │ └── tree_based_merge/ # the postprocess of SelfLog ├── logs/ │ └── ...... # parsing log files ├── online_selfLog/ # online version of SelfLog │ ├── is_new_log # log hitter │ ├── log_pruduce # streaming log production │ └── online_run # test the efficient of SelfLog ├── PSQL/ # Prompt database recall method based on PostgreSQL │ ├── model # the embedding model of SelfLog │ ├── conConfig # connect psql setting │ ├── exampleToPSQL # algorithm startup candidate set written to psql │ └── findTopKexam # recall examples ├── CONSTANT # hyperparameter configuration items ├── llmAPIsetting # llm address url and key ├── prompt # llm prompt format ├── run.py # test the effect of SelfLog on the dataset └── README.md

Quick start

Preparation

Environment Installation

• Prompt Database We use psql with the vector plugin to implement a method for retrieving and recalling related logs based on semantic similarity. You can also use other databases for your purposes. > 1. Install PostgresSQL > 2. Creat table such as CREATE TABLE IF NOT EXISTS public.log_template ( "ID" integer NOT NULL DEFAULT nextval('id_seq'::regclass), log text COLLATE pg_catalog."default", template text COLLATE pg_catalog."default", "logVector" vector, CONSTRAINT seflog_pkey PRIMARY KEY ("ID") );
• Python > 1. Install python >= 3.8 > 2. pip install -r requirements.txt

Set settings

• LLM API > 1. API-key > 2. model url
• Candidates to prompt database > 1. cd PSQL > 2. python exampleToPSQL.py
• Effect evaluation > 1. python run.py

The analysis results will be stored in the log directory. * Efficiency evaluation

1. cd online_selfLog
2. download full dataset
3. python log_pruduce.py