diff --git a/ai2/log_analyzer.py b/ai2/log_analyzer.py new file mode 100644 index 0000000..04b3942 --- /dev/null +++ b/ai2/log_analyzer.py @@ -0,0 +1,113 @@ +import pandas as pd +from langchain.output_parsers import ResponseSchema, StructuredOutputParser +from langchain.prompts import PromptTemplate +from langchain_google_vertexai import VertexAI +from pinecone import Pinecone + + +class LLMLogAnalyzer: + def __init__(self, pinecone_api_key: str, index_name: str, llm_args: dict) -> None: + self.llm = VertexAI(**llm_args) + + pc = Pinecone(api_key=pinecone_api_key) + self.db = pc.Index(index_name) + + # Define output parser and generate format instruction + severity_schema = ResponseSchema( + name="severity", + description='The severity of the analysis feedback is categorized as follows: "ERROR" indicates severe problems that require immediate scaling action, "WARNING" suggests less severe issues that nonetheless require caution, and "INFO" denotes the absence of problems.', + ) + cpu_schema = ResponseSchema( + name="cpu", + description="Number of CPUs to add(positive) or remove(negative) to each instance.", + type="int", + ) + mem_schema = ResponseSchema( + name="memory", + description="Amount of memory in MB to add(positive) or remove(negative) to each instance.", + type="int", + ) + instance_schema = ResponseSchema( + name="instance", + description="Amount of instances to add(positive) or remove(negative).", + type="int", + ) + message_schema = ResponseSchema( + name="message", + description="In-depth and well formatted analysis in markdown format.", + ) + self.output_parser = StructuredOutputParser.from_response_schemas( + [severity_schema, cpu_schema, mem_schema, instance_schema, message_schema] + ) + self.format_instruction = self.output_parser.get_format_instructions() + + # Define model and prompt template + prompt_template = PromptTemplate.from_template( + """\ +The following text contains log data for a Google Cloud Run application. \ +This data is presented in CSV format and encompasses the most recent {time_span} minutes: +{log_data} + +Some heuristic analysis has been performed beforehand, the following text is a summary of the analysis: +{heuristic_analysis} + +Your task is to provide an in-depth analysis based on the provided log data and heuristic analysis feedback, \ +and scale the application by providing the number of CPUs, amount of memory in MB, and amount of instances to add(positive) or remove(negative). +The system will automatically scale the application based on your feedback. + +{format_instruction} +""" + ) + + # Define analysis chain + self.analysis_chain = prompt_template | self.llm + + def _heuristic_analysis(self, log_df: pd.DataFrame) -> str: + feedback = "" + + # Analyze CPU utilization + cpu_label = "Container CPU Utilization (%)" + for i in range(len(log_df) - 1): + if log_df.iloc[i][cpu_label] > 40 and log_df.iloc[i + 1][cpu_label] > 40: + feedback += f"- Container CPU Utilization (%) is above 40% for two minutes, at {log_df.iloc[i]['Time']} and {log_df.iloc[i + 1]['Time']}\n" + + # Analyze memory utilization + mem_label = "Container Memory Utilization (%)" + for i in range(len(log_df) - 1): + if ( + log_df.iloc[i][mem_label] > 50 + and log_df.iloc[i + 1][mem_label] > 50 + ): + feedback += f"- Container Memory Utilization (%) is above 50% for two minutes, at {log_df.iloc[i]['Time']} and {log_df.iloc[i + 1]['Time']}\n" + + # Analyze remaining task count in queue + for i in range(len(log_df)): + if log_df.iloc[i]["Remaining Task Count in Queue"] > 100: + feedback += f"- Remaining Task Count in Queue is above 100 at {log_df.iloc[i]['Time']}\n" + + # Analyze average task execution time + for i in range(len(log_df)): + if log_df.iloc[i]["Average Task Execution Time"] > 30: + feedback += f"- Average Task Execution Time is above 30 seconds at {log_df.iloc[i]['Time']}\n" + + return feedback + + def analyze_log(self, log_df: pd.DataFrame) -> dict: + # Perform heuristic analysis to aid the model + heuristic_feedback = self._heuristic_analysis(log_df) + + # Invoke the model + feedback = self.analysis_chain.invoke( + { + "log_data": log_df.to_csv(index=False), + "time_span": len(log_df), + "heuristic_analysis": heuristic_feedback, + "format_instruction": self.format_instruction, + } + ) + + # Parse the output + feedback_dict = self.output_parser.parse(feedback) + feedback_dict["metric_dataframe"] = log_df + feedback_dict["timestamp"] = log_df.iloc[-1]["Time"] + return feedback_dict diff --git a/poetry.lock b/poetry.lock index 8b1d38f..c767b2f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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