diff --git a/.env.example b/.env.example index 7804b61..d24ae1e 100644 --- a/.env.example +++ b/.env.example @@ -111,4 +111,8 @@ DASHBOARD_PORT=5173 APP_URL=http://localhost:${DASHBOARD_PORT} # PagerDuty -PAGERDUTY_SECRET="put your secret" \ No newline at end of file +PAGERDUTY_SECRET="put your secret" + +# Log parser +LOG_PARSER_PORT=3004 +LOG_PARSER_URL=http://log-parser:${LOG_PARSER_PORT} \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json index af6650c..f8abc8f 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -38,12 +38,17 @@ "type": "node", "request": "launch", "name": "API: Debug TypeScript", - "program": "${workspaceFolder}/services/api/node_modules/ts-node/dist/bin.js", - "args": ["${file}"], + "runtimeExecutable": "yarn", + "args": [ + "runFile", + "${file}" + // "Q2HI9P83ZC9UXT", + // "66841ebc489b57392f102e08", + // "PagerDuty" + ], "sourceMaps": true, - "cwd": "${workspaceFolder}/services/api/", - "internalConsoleOptions": "neverOpen", - "envFile": "${workspaceFolder}/services/api/.env" + "cwd": "${workspaceFolder}/services/api", + "internalConsoleOptions": "neverOpen" }, { "type": "node", @@ -73,6 +78,14 @@ "program": "src/main.py", "cwd": "${workspaceFolder}/services/data-processor/", "justMyCode": false + }, + { + "type": "debugpy", + "name": "Log Parser: Debug", + "request": "launch", + "program": "src/main.py", + "cwd": "${workspaceFolder}/services/log-parser/", + "justMyCode": false } ] } diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 2bd6d8e..63d656a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -91,6 +91,7 @@ We work in a monorepo manner using [yarn](https://yarnpkg.com/) and [nx](https:/ - `slackbot` - the slackbot code. Contains the logic of our Slack app. - `dashboard` - the web ui code. Contains the code of the web app, which allows users to configure their organization. - `data-processor` - contains the data processor code. This service is used to build the knowledge base that is needed in order to perform an extensive investigation with rich context. +- `log-parser` - performs simple log aggregation/clustering. **Other important folders/files** diff --git a/config/litellm/config.example.yaml b/config/litellm/config.example.yaml index ad4d5d7..b717b97 100644 --- a/config/litellm/config.example.yaml +++ b/config/litellm/config.example.yaml @@ -19,7 +19,7 @@ model_list: ### OpenAI (Default) #### - model_name: chat-model litellm_params: - model: openai/gpt-3.5-turbo-1106 + model: openai/gpt-4o-mini api_key: os.environ/OPENAI_API_KEY - model_name: embedding-model litellm_params: diff --git a/docker-compose.common.yml b/docker-compose.common.yml index 11439f6..37a5f35 100644 --- a/docker-compose.common.yml +++ b/docker-compose.common.yml @@ -154,6 +154,7 @@ services: - DATA_PROCESSOR_URL - PAGERDUTY_SECRET - LITELLM_URL + - LOG_PARSER_URL - NODE_ENV=development - METADATA_ENCRYPTION_KEY=f78d8202-bb2a-49fe-bd63-a586cd921226 - ENCRYPTION_SALT=1234567890123456 @@ -205,6 +206,14 @@ services: - ${DATA_DIR}/secrets:${SECRET_MANAGER_DIRECTORY} depends_on: - mongo + log-parser-common: + container_name: log-parser + profiles: ["app"] + environment: + - TELEMETRY_ENABLED + - PORT=${LOG_PARSER_PORT} + ports: + - "${LOG_PARSER_PORT}:${LOG_PARSER_PORT}" dashboard-common: container_name: dashboard profiles: ["app"] diff --git a/docker-compose.images.yml b/docker-compose.images.yml index 14b8ea0..385a832 100644 --- a/docker-compose.images.yml +++ b/docker-compose.images.yml @@ -56,6 +56,11 @@ services: file: docker-compose.common.yml service: data-processor-common image: merlinnco/data-processor:latest + log-parser: + extends: + file: docker-compose.common.yml + service: log-parser-common + image: merlinnco/log-parser:latest dashboard: extends: file: docker-compose.common.yml diff --git a/docker-compose.yml b/docker-compose.yml index 5148663..cc5a607 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -61,6 +61,12 @@ services: service: data-processor-common build: context: services/data-processor + log-parser: + extends: + file: docker-compose.common.yml + service: log-parser-common + build: + context: services/log-parser dashboard: extends: file: docker-compose.common.yml diff --git a/packages/db/src/types.ts b/packages/db/src/types.ts index ab69ee2..e8e8000 100644 --- a/packages/db/src/types.ts +++ b/packages/db/src/types.ts @@ -124,9 +124,7 @@ export interface BaseConnection { _id: Types.ObjectId; vendor: IVendor; organization: IOrganization; - settings?: { - tools?: Record; - }; + settings?: Record; type: "basic" | "oauth"; createdAt: Date; updatedAt: Date; @@ -180,6 +178,9 @@ export interface PagerDutyIntegration extends BaseConnection { scope: string; expires_in: number; }; + settings: { + slackChannelId: string; + }; } export interface OpsgenieIntegration extends BaseConnection { diff --git a/services/api/.env.dev b/services/api/.env.dev index 84adcef..5564871 100644 --- a/services/api/.env.dev +++ b/services/api/.env.dev @@ -13,4 +13,5 @@ CHROMA_HOST="http://localhost:8000" CHROMA_API_KEY="secret-token" DATA_PROCESSOR_URL=http://localhost:3002 LITELLM_URL=http://localhost:4000 +LOG_PARSER_URL=http://localhost:3004 TELEMETRY_ENABLED=true \ No newline at end of file diff --git a/services/api/package.json b/services/api/package.json index 1d30283..a001f86 100644 --- a/services/api/package.json +++ b/services/api/package.json @@ -45,7 +45,8 @@ "lint": "nx lint --ext ts", "test": "jest", "typecheck": "tsc -p tsconfig.json --noEmit", - "deploy": "npx ts-node ../../tools/scripts/deploy.ts api" + "deploy": "npx ts-node ../../tools/scripts/deploy.ts api", + "runFile": "npx dotenv-cli -o -e ../../.env -e .env.dev npx ts-node" }, "devDependencies": { "@babel/preset-typescript": "^7.23.3", diff --git a/services/api/src/agent/prompts.ts b/services/api/src/agent/prompts.ts index 4a8984e..e0dbf1c 100644 --- a/services/api/src/agent/prompts.ts +++ b/services/api/src/agent/prompts.ts @@ -20,6 +20,23 @@ export const prefix = { Begin! `, + investigationLean: ` + You are an expert on-call engineer called Merlinn. Your mission is to investigate incidents in Production and provide findings to the responders, with as much information as possible. + + Given the contextual information, produce a meaningful summarization about the data provided. + Don't write a report, write in free text, 5 sentences max. Be very concise and produce a short answer in a human readable format. + + Begin! + + Incident: + {incident} + + Additional investigation information: + {additionalInfo} + + Contextual information: + {context} + `, conversation: ` You are a smart AI assistant called Merlinn. Your mission is to help on-call developers and SREs investigate production incidents. @@ -50,6 +67,85 @@ export const prefix = { captionImage: ` Please describe what you see in this image `, + generateQueries: ` + Hi! You are tasked with retrieving information about the following production incident: + + {incident} + + You have access to a semantic search engine (VectorDB), where you can fetch historical & related information about this issue. Please create {nQueries} different queries that you would want to search against this search engine. + + Your queries should be: + * Related to the incident above + * Short and simple. No long sentences. + * Abstract and expressive, with no IP addresses & numbers. + + + You should return your answer as JSON. It should contain 1 key called "queries", and it should be a list. For instance, here is an example response: + \`\`\`json + {{"queries": ["Service X issue", "500 error", "User could not pay issue"]}} + \`\`\` + + + IMPORTANT: Please respond only in JSON. + + Begin! + `, + verifyDocument: ` + Hi! You are tasked with verifying whether a document is actually relevant to a source information. The source information is a production incident information. You are tasked with judging whether a document is relevant. + + For example, given the following incident: + \`\`\` + Title: Coralogix Alert: Service data-processor has high CPU usage + Source: PagerDuty + Time: 5 months ago + Additional information: {{"Application":"demo-app","CompanyId":4014214}} + \`\`\` + + And the following document: + \`\`\` + Does someone know where do we save our finance reports? + \`\`\` + + You should return false. IMPORTANT: return only true or false. + + Begin! + + Incident: + {incident} + + Document: + {document} + `, + extractLogStructureKeys: ` + Given a log record, return the key paths of the severity and message in a JSON format. + + Examples: + Log records: + [{{"message": "Successfully updated user 123", "timestamp": "some-time", "severityText": "INFO"}}, + {{"message": "Successfully updated user 456", "timestamp": "some-time", "severityText": "INFO"}}] + + Output: + {{ + "severityKey": "severityText", + "messageKey": "message" + }} + + Log record: + [{{"timestamp": "some-time", "severity": {{"severityNumber": 3, "severityText": "INFO"}}, "logRecord": {{"body": "Successfully updated user 123"}}}}, + {{"timestamp": "some-time2", "severity": {{"severityNumber": 3, "severityText": "INFO"}}, "logRecord": {{"body": "Successfully updated user 456"}}}} + ] + + Output: + {{ + "severityKey": "severity.severityText", + "messageKey": "logRecord.body" + }} + + Begin! + + Log records: + {logRecords} + `, }; export const investigationTemplate = ChatPromptTemplate.fromMessages([ @@ -76,3 +172,19 @@ export const conversationIssuesTemplate = ChatPromptTemplate.fromMessages([ export const summarizeReadmePrompt = PromptTemplate.fromTemplate( prefix.summarizeReadme, ); + +export const generateQueriesPrompt = PromptTemplate.fromTemplate( + prefix.generateQueries, +); + +export const verifyDocumentPrompt = PromptTemplate.fromTemplate( + prefix.verifyDocument, +); + +export const investigationLeanTemplate = PromptTemplate.fromTemplate( + prefix.investigationLean, +); + +export const extractLogStructureKeysPrompt = PromptTemplate.fromTemplate( + prefix.extractLogStructureKeys, +); diff --git a/services/api/src/agent/rag/utils.ts b/services/api/src/agent/rag/utils.ts index 6c8173e..ec7c454 100644 --- a/services/api/src/agent/rag/utils.ts +++ b/services/api/src/agent/rag/utils.ts @@ -1,5 +1,6 @@ import { PineconeVectorStore } from "./pinecone"; import { ChromaDBVectorStore } from "./chromadb"; +import type { Document } from "./types"; export function getVectorStore( indexName: string, @@ -26,3 +27,15 @@ export function getVectorStore( throw new Error(`Invalid index source: ${indexType}`); } } + +export function nodesToText(documents: Document[]) { + const formattedNodes = documents.map( + (document, index) => + `Document: ${index + 1}\n + Source: ${document.metadata.source}\n + Score: ${document.score}\n + Metadata: ${JSON.stringify(document.metadata)}\n + Text: ${document.text}`, + ); + return formattedNodes.join("\n\n"); +} diff --git a/services/api/src/agent/tools/base.ts b/services/api/src/agent/tools/base.ts index e046efb..8af11a6 100644 --- a/services/api/src/agent/tools/base.ts +++ b/services/api/src/agent/tools/base.ts @@ -15,7 +15,7 @@ export const createAgent = async ( tools: Tool[], template: ChatPromptTemplate, ) => { - const apiKey = process.env.OPENAI_API_KEY; + const apiKey = "secret-key"; // This is a dummy variable. LiteLLM is the one responsible for the actual API key const baseURL = process.env.LITELLM_URL; const model = new ChatOpenAI({ diff --git a/services/api/src/agent/tools/coralogix/logs_expert.ts b/services/api/src/agent/tools/coralogix/logs_expert.ts index 63f8bd0..c247e7a 100644 --- a/services/api/src/agent/tools/coralogix/logs_expert.ts +++ b/services/api/src/agent/tools/coralogix/logs_expert.ts @@ -1,41 +1,61 @@ import { z } from "zod"; import { DynamicStructuredTool } from "langchain/tools"; -import CallbackHandler from "langfuse-langchain"; -import { ChatPromptTemplate, MessagesPlaceholder } from "langchain/prompts"; +import { PromptTemplate } from "langchain/prompts"; import { CoralogixIntegration } from "@merlinn/db"; -import util from "util"; -import { createAgent } from "../base"; import { DATAPRIME_CHEATSHEET } from "./constants"; import { getCommonLogFields, getPrettyLogSample } from "./utils"; -import { default as readLogs } from "./read_logs"; -import { default as getFieldsValues } from "./get_fields_values"; -import { RunContext } from "../../../agent/types"; -import { AnswerContext, LLMCallbacks } from "../../../agent/callbacks"; import { buildOutput } from "../utils"; -import { isLangfuseEnabled } from "../../../utils/ee"; -import { Callbacks } from "langchain/callbacks"; - -const TOOL_LOADERS = [readLogs, getFieldsValues]; +import { JsonOutputParser } from "langchain/schema/output_parser"; +import { chatModel } from "../../../agent/model"; +import { getTimestamp, timeframe2values } from "../../../utils/dates"; +import { CoralogixClient } from "../../../clients"; +import { RunContext } from "../../../agent/types"; const PROMPT_TEMPLATE = ` -You are a Coralogix logs expert. Your mission is to fetch logs, based on users requests. - -At your disposal, you have a read_logs tool. Given a DataPrime query and a timeframe, it will try to fetch the logs from Coralogix. -DataPrime is a specialized query language that allows you to search logs in Coralogix. +You are a Coralogix logs expert. Given a request in natural language, you should generate {nQueries} queries in a DataPrime syntax. +DataPrime is Coralogix's proprietary query language that allows you to query logs in a more structured way. Here is a cheatsheet of DataPrime query language: -${DATAPRIME_CHEATSHEET} +{cheatsheet} Here are the common fields that you can use in your query (they were taken from the environment itself): -%s +{commonFields} Here is a sample of logs so you'd know how they look like: -%s +{logSample} + +You should return your answer as a block of Thought, Observation and Answer. +The thought section should contain a thought that you have about the request. +The observation section should contain an observation on log sample/cheatsheet/common fields. +The answer section should contain a JSON with only one key called "queries", and it should be a list of valid DataPrime queries. + +For instance, given the following logSample + request: + +Log sample +resource.attributes.service.name: recommendationservice +logRecord.severityText: WARN +logRecord.body: Receive ListRecommendations for product ids:['OLJCESPC7Z', 'L9ECAV7KIM', '66VCHSJNUP', '6E92ZMYYFZ', 'LS4PSXUNUM'] + +Request: +"Please fetch all the warning logs for the recommendationservice" + +Your answer should be as follows: +Thought: I think that the request is asking for logs with a specific severity level for a specific service, in this case, the "recommendationservice". +Observation: The log sample contains the service name and the severity level. The fields are called "resource.attributes.service.name" and "logRecord.severityText". +Answer: +\`\`\`json +{{"queries": ["source logs | filter resource.attributes.service.name == 'recommendationservice' && logRecord.severityText == 'WARN'"]}} +\`\`\` + +Please create variations of the queries to cover more ground. Try to make some of them wider and some of them more specific. +Start from the service level and then go deeper into the logs. -IMPORTANT NOTE: -- Before querying based on a field, please call the get_distinct_log_values tool to see the unique values for that field. +IMPORTANT! Always look at the log sample and the common fields to understand the structure of the logs, so your queries are valid. +IMPORTANT! Don't include timestamp filters. We add them in the metadata of the query, not in the query itself. -If you can't find the answer, please ask clarifying questions and get some help. +Begin! + +{request} `; const TOOL_DESCRIPTION = ` @@ -49,56 +69,84 @@ export default async function ( integration: CoralogixIntegration, context: RunContext, ) { + console.log(context); + const { logsKey } = integration.credentials; - const { region } = integration.metadata; + const { region, domainURL } = integration.metadata; - const tools = await Promise.all( - TOOL_LOADERS.map((loader) => loader(integration)), - ); const commonFields = await getCommonLogFields(logsKey, region); - const logSample = await getPrettyLogSample(logsKey, region, 2); - const prompt = util.format( - PROMPT_TEMPLATE, - JSON.stringify(commonFields), - JSON.stringify(logSample), - ); - const template = ChatPromptTemplate.fromMessages([ - ["ai", prompt], - new MessagesPlaceholder("history"), - ["human", "{input}"], - new MessagesPlaceholder("agent_scratchpad"), - ]); - - const agent = await createAgent(tools, template); - return new DynamicStructuredTool({ name: "logs_expert_tool", description: TOOL_DESCRIPTION, func: async ({ request }) => { try { - const answerContext = isLangfuseEnabled() - ? new AnswerContext(context.trace) - : new AnswerContext(); - const globalCallbacks = new LLMCallbacks(answerContext); - - const callbacks: Callbacks = [globalCallbacks]; - if (isLangfuseEnabled()) { - callbacks.push( - new CallbackHandler({ - root: context.trace!.span({}), - secretKey: process.env.LANGFUSE_SECRET_KEY as string, - publicKey: process.env.LANGFUSE_PUBLIC_KEY as string, - baseUrl: process.env.LANGFUSE_HOST as string, + const prompt = await PromptTemplate.fromTemplate( + PROMPT_TEMPLATE, + ).format({ + nQueries: 3, + cheatsheet: DATAPRIME_CHEATSHEET, + commonFields: commonFields.join(", "), + logSample: logSample, + request, + }); + + const parser = new JsonOutputParser(); + const { content } = await chatModel.invoke(prompt); + const { queries } = await parser.parse(content as string); + if (!queries || queries.length === 0) { + throw new Error("No queries generated"); + } + + const [amount, scale] = timeframe2values["Last 24 hours"]; + const startDate = getTimestamp({ amount, scale }); + const endDate = new Date().toISOString(); + const client = new CoralogixClient({ logsKey }, region); + const results = ( + await Promise.all( + queries.map(async (query: string) => { + const result = await client.getLogs({ + syntax: "QUERY_SYNTAX_DATAPRIME", + query, + startDate, + endDate, + }); + if (!result.result?.results) { + return `Coraloigx returned empty result. Information: ${JSON.stringify( + result, + )}`; + } + + return result; }), + ) + ) + .filter((result) => result.result?.results?.length > 0) + .reduce( + (acc, val, index) => { + acc.queries.push(queries[index]); + acc.results.push(val.result.results); + return acc; + }, + { queries: [], results: [] }, ); - } - const { output: answer } = await agent.call( - { input: request }, - { callbacks }, + + const sources = results.queries.map((query: string) => { + const link = `${domainURL}/#/query-new/logs?query=${encodeURIComponent( + query, + )}&time=from:${startDate},to:${endDate}&page=0&querySyntax=dataprime&permalink=true`; + return `[Coralogix Logs Link](${link})`; + }); + + // TODO: this is a temporary solution, to look at more + // specific and fewer log results for now. + const logs = results.results.sort( + (a: string[], b: string[]) => a.length - b.length, ); - const output = buildOutput(answer, answerContext.getSources()); + const logsStr = JSON.stringify(logs).slice(0, 10000); + const output = buildOutput(logsStr, sources); + return output; } catch (error) { console.error(error); diff --git a/services/api/src/agent/tools/coralogix/read_logs.ts b/services/api/src/agent/tools/coralogix/read_logs.ts index 91131ec..31f28f8 100644 --- a/services/api/src/agent/tools/coralogix/read_logs.ts +++ b/services/api/src/agent/tools/coralogix/read_logs.ts @@ -3,27 +3,14 @@ import { z } from "zod"; import { DynamicStructuredTool } from "langchain/tools"; import type { CoralogixIntegration } from "@merlinn/db"; import type { CoralogixRegionKey } from "../../../types"; -import { Timescale, getTimestamp } from "../../../utils/dates"; +import { + Timeframe, + getTimestamp, + timeframe2values, +} from "../../../utils/dates"; import { buildOutput } from "../utils"; import { CoralogixClient } from "../../../clients"; -const timeframe2values = { - "Last 1 minute": [1, "minutes"], - "Last 2 minutes": [2, "minutes"], - "Last 5 minutes": [5, "minutes"], - "Last 15 minutes": [15, "minutes"], - "Last 30 minutes": [30, "minutes"], - "Last 1 hour": [1, "hours"], - "Last 2 hours": [2, "hours"], - "Last 6 hours": [6, "hours"], - "Last 12 hours": [12, "hours"], - "Last 24 hours": [24, "hours"], - "Last 2 days": [2, "days"], - "Last 3 days": [3, "days"], - "Last 5 days": [5, "days"], - "Last 7 days": [7, "days"], -} as Record; - export const fetchLogs = async ( query: string, startDate: string, @@ -104,20 +91,20 @@ export default async function (integration: CoralogixIntegration) { ), timeframe: z .enum([ - "Last 1 minute", - "Last 2 minutes", - "Last 5 minutes", - "Last 15 minutes", - "Last 30 minutes", - "Last 1 hour", - "Last 2 hours", - "Last 6 hours", - "Last 12 hours", - "Last 24 hours", - "Last 2 days", - "Last 3 days", - "Last 5 days", - "Last 7 days", + // Timeframe.Last1Minute, + // Timeframe.Last2Minutes, + // Timeframe.Last5Minutes, + // Timeframe.Last15Minutes, + // Timeframe.Last30Minutes, + // Timeframe.Last1Hour, + // Timeframe.Last2Hours, + // Timeframe.Last6Hours, + // Timeframe.Last12Hours, + Timeframe.Last24Hours, + // Timeframe.Last2Days, + // Timeframe.Last3Days, + // Timeframe.Last5Days, + // Timeframe.Last7Days, ]) .describe("The period for which you wish to search the logs."), }), diff --git a/services/api/src/agent/tools/index.ts b/services/api/src/agent/tools/index.ts index 96181dd..5ef3ea9 100644 --- a/services/api/src/agent/tools/index.ts +++ b/services/api/src/agent/tools/index.ts @@ -14,15 +14,10 @@ import { createToolLoaders as createStaticToolLoaders } from "./static"; import { toolLoaders as mongodbToolLoaders } from "./mongodb"; import { toolLoaders as jaegerToolLoaders } from "./jaeger"; import { toolLoaders as prometheusToolLoaders } from "./prometheus"; -import { Tool } from "./types"; +import { Tool, ToolLoader } from "./types"; import { RunContext } from "../types"; import { dummyTool } from "./dummy"; -type ToolLoader = ( - integration: T, - context: RunContext, -) => Promise; - export const compileTools = async ( toolLoaders: ToolLoader[], integration: T, @@ -63,7 +58,6 @@ export const createTools = async ( ) => { const tools = [] as Tool[]; - // Coralogix const [ coralogixTools, githubTools, diff --git a/services/api/src/agent/tools/static/semantic_search.ts b/services/api/src/agent/tools/static/semantic_search.ts index 8c97f6a..d024212 100644 --- a/services/api/src/agent/tools/static/semantic_search.ts +++ b/services/api/src/agent/tools/static/semantic_search.ts @@ -3,19 +3,8 @@ import { DynamicStructuredTool } from "langchain/tools"; import { RunContext } from "../../types"; import { buildOutput } from "../utils"; import { indexModel } from "@merlinn/db"; -import { getVectorStore, Document } from "../../rag"; +import { getVectorStore, nodesToText } from "../../rag"; -function nodesToText(documents: Document[]) { - const formattedNodes = documents.map( - (document, index) => - `Document: ${index + 1}\n - Source: ${document.metadata.source}\n - Score: ${document.score}\n - Metadata: ${JSON.stringify(document.metadata)}\n - Text: ${document.text}`, - ); - return formattedNodes.join("\n\n"); -} export default async function (context: RunContext) { const index = await indexModel.getOne({ organization: context.organizationId, @@ -30,7 +19,7 @@ export default async function (context: RunContext) { return "Knowledge base is not set up. Tool is not available."; } const vectorStore = getVectorStore(index.name, index.type); - const documents = await vectorStore.query({ query, topK: 5 }); + const documents = await vectorStore.query({ query, topK: 3 }); const text = nodesToText(documents); // Create sources @@ -77,7 +66,17 @@ export default async function (context: RunContext) { return { url: manualUrl, title }; } case "PagerDuty": { - return document.metadata.link; + return { + url: document.metadata.link, + title: "PagerDuty Alert", + }; + } + case "Confluence": { + const { url, title } = document.metadata; + return { url, title }; + } + default: { + throw new Error("Unsupported source"); } } })(); diff --git a/services/api/src/agent/tools/types.ts b/services/api/src/agent/tools/types.ts index 1c94c0f..c5cbb5f 100644 --- a/services/api/src/agent/tools/types.ts +++ b/services/api/src/agent/tools/types.ts @@ -1,3 +1,10 @@ +import { IIntegration } from "@merlinn/db"; import { DynamicStructuredTool, DynamicTool } from "langchain/tools"; +import { RunContext } from "../types"; export type Tool = DynamicStructuredTool | DynamicTool; + +export type ToolLoader = ( + integration: T, + context: RunContext, +) => Promise; diff --git a/services/api/src/agent/types.ts b/services/api/src/agent/types.ts index 6d44036..11634fe 100644 --- a/services/api/src/agent/types.ts +++ b/services/api/src/agent/types.ts @@ -35,13 +35,7 @@ export interface RunContext { env: string; organizationName: string; organizationId: string; - context: - | "chat" - | "chat-github" - | "trigger-pagerduty" - | "trigger-opsgenie" - | "trigger-alertmanager" - | "trigger-local-script"; + context: string; } export interface RunAgentParams { diff --git a/services/api/src/clients/coralogix/client.ts b/services/api/src/clients/coralogix/client.ts index af4b779..e11d427 100644 --- a/services/api/src/clients/coralogix/client.ts +++ b/services/api/src/clients/coralogix/client.ts @@ -34,6 +34,22 @@ export class CoralogixClient { }); } + getRawLogs = async (request: CoralogixQueryRequest): Promise => { + const { query, startDate, endDate, syntax } = request; + const { apiURL } = domains.management[this.region]; + const metadata = { syntax, startDate, endDate }; + try { + const { data } = await this.axios.post(apiURL, { + query, + metadata, + }); + return data; + } catch (error) { + console.log(error); + throw error; + } + }; + getLogs = async ( request: CoralogixQueryRequest, ): Promise => { @@ -41,34 +57,12 @@ export class CoralogixClient { const { apiURL } = domains.management[this.region]; const metadata = { syntax, startDate, endDate }; try { - const { data: result } = await this.axios.post( - apiURL, - { query, metadata }, - ); + const { data } = await this.axios.post(apiURL, { + query, + metadata, + }); - if (typeof result === "string") { - const objects = (result as string) - .split("\n") - .filter((obj: string) => !!obj) as string[]; - const results = objects.map((obj) => JSON.parse(obj)); - const logs = results - .reduce( - (total, current) => [...total, ...(current.result?.results || [])], - [], - ) - .map((o: CoralogixLogRecord) => ({ - ...o, - userData: JSON.stringify(flatten(JSON.parse(o.userData))), - })); - return { result: { results: logs } }; - } else { - if (result.result) { - result.result.results = result.result.results.map((o) => ({ - ...o, - userData: JSON.stringify(flatten(JSON.parse(o.userData))), - })); - } - } + const result = this.parseResult(data); return result; } catch (error) { console.log(error); @@ -118,4 +112,33 @@ export class CoralogixClient { return null; } }; + + parseResult = ( + result: string | CoralogixQueryResult, + ): CoralogixQueryResult => { + if (typeof result === "string") { + const objects = (result as string) + .split("\n") + .filter((obj: string) => !!obj) as string[]; + const results = objects.map((obj) => JSON.parse(obj)); + const logs = results + .reduce( + (total, current) => [...total, ...(current.result?.results || [])], + [], + ) + .map((o: CoralogixLogRecord) => ({ + ...o, + userData: JSON.stringify(flatten(JSON.parse(o.userData))), + })); + return { result: { results: logs } }; + } else { + if (result.result) { + result.result.results = result.result.results.map((o) => ({ + ...o, + userData: JSON.stringify(flatten(JSON.parse(o.userData))), + })); + } + } + return result; + }; } diff --git a/services/api/src/routers/integrations.ts b/services/api/src/routers/integrations.ts index be53ff4..3789265 100644 --- a/services/api/src/routers/integrations.ts +++ b/services/api/src/routers/integrations.ts @@ -100,7 +100,6 @@ router.put( getDBUser, catchAsync(async (req: Request, res: Response) => { const { id } = req.params; - const { tools } = req.body; const integration = await integrationModel.getOneById(id); if (!integration) { @@ -113,8 +112,6 @@ router.put( throw new AppError("User is not allowed to perform this action", 403); } - const { settings = {} } = integration; - integration.settings = { ...settings.tools, ...tools }; return res.status(200).json({ integration }); }), ); diff --git a/services/api/src/routers/webhooks/pagerduty/processor.ts b/services/api/src/routers/webhooks/pagerduty/processor.ts new file mode 100644 index 0000000..2aa13ba --- /dev/null +++ b/services/api/src/routers/webhooks/pagerduty/processor.ts @@ -0,0 +1,116 @@ +import { + IIntegration, + IWebhook, + PagerDutyIntegration, + SlackIntegration, + integrationModel, +} from "@merlinn/db"; +import { PagerDutyWebhookEvent } from "../../../types"; +import { secretManager } from "../../../common/secrets"; +import { SlackClient } from "../../../clients"; +import { postInitialStatus } from "../utils"; +import { runRCA } from "../../../services/rca"; +import { RunContext } from "../../../agent/types"; +import { isLangfuseEnabled } from "../../../utils/ee"; +import { generateTrace } from "../../../agent/helper"; +import { EventType, SystemEvent, events } from "../../../events"; +import { MessageMetadata } from "@slack/bolt"; + +export async function processWebhook( + webhook: IWebhook, + event: PagerDutyWebhookEvent, +) { + const { organization } = webhook; + const organizationName = organization.name; + const organizationId = String(organization._id); + + const { event: pdEvent } = event; + + const integrations = (await integrationModel + .get({ + organization: organization._id, + }) + .populate("vendor")) as IIntegration[]; + + // Get integrations to Slack and PagerDuty + let slackIntegration = integrations.find( + (integration) => integration.vendor.name === "Slack", + ) as SlackIntegration; + const pagerdutyIntegration = integrations.find( + (integration) => integration.vendor.name === "PagerDuty", + ) as PagerDutyIntegration; + + if (!slackIntegration) { + throw new Error("Slack integration was not found"); + } else if (!pagerdutyIntegration) { + throw new Error("PagerDuty integration was not found"); + } + + slackIntegration = ( + await secretManager.populateCredentials([slackIntegration]) + )[0] as SlackIntegration; + + const channelId = pagerdutyIntegration.settings.slackChannelId as string; + + const { access_token: slackToken } = slackIntegration.credentials; + const slackClient = new SlackClient(slackToken); + + const messages = await slackClient.getChannelHistoryGracefully(channelId); + const pdMessage = messages?.find((message) => + message.text?.includes(pdEvent.data.id), + ); + if (!pdMessage) { + throw new Error("Could not find Slack message"); + } + + await postInitialStatus(slackToken, channelId, pdMessage.ts!); + + const analysis = await runRCA( + pdEvent.data.id, + "PagerDuty", + String(organization._id), + ); + + const context: RunContext = { + organizationName, + organizationId, + env: process.env.NODE_ENV as string, + eventId: pdEvent.data.id, + context: "trigger-pagerduty", + }; + if (isLangfuseEnabled()) { + const trace = generateTrace({ ...context }); + context.trace = trace; + } + + const systemEvent: SystemEvent = { + type: EventType.answer_created, + entityId: String(organization._id), + payload: { + organizationId, + organizationName, + env: process.env.NODE_ENV as string, + context: "trigger-pagerduty", + }, + }; + + events.publish(systemEvent); + + // Post a reply to the thread + const metadata: MessageMetadata = { + event_type: systemEvent.type, + event_payload: systemEvent.payload, + }; + + const response = await slackClient.postReply({ + channelId, + ts: pdMessage?.ts as string, + text: analysis, + metadata, + }); + + const { ok, ts } = response; + if (ok) { + await slackClient.addFeedbackReactions(channelId, ts!); + } +} diff --git a/services/api/src/routers/webhooks/pagerduty/router.ts b/services/api/src/routers/webhooks/pagerduty/router.ts index 07d08ee..edc0e2b 100644 --- a/services/api/src/routers/webhooks/pagerduty/router.ts +++ b/services/api/src/routers/webhooks/pagerduty/router.ts @@ -1,159 +1,41 @@ import express, { Request, Response } from "express"; -import { SlackClient } from "../../../clients"; -import { runAgent } from "../../../agent"; -import { - IIntegration, - PagerDutyIntegration, - SlackIntegration, - integrationModel, -} from "@merlinn/db"; -import { MessageMetadata } from "@slack/bolt"; -import { PagerDutyWebhookEvent } from "../../../types"; -import { postInitialStatus } from "../utils"; import { checkAlertsQuota, checkWebhookSecret, } from "../../../middlewares/webhooks"; import { checkPagerDutySignature } from "./utils"; -import { parseAlertToPrompt } from "../../../services/alerts"; -import { EventType, SystemEvent, events } from "../../../events"; -import { investigationTemplate } from "../../../agent/prompts"; -import { chatModel } from "../../../agent/model"; -import { catchAsync } from "../../../utils/errors"; -import { AppError, ErrorCode } from "../../../errors"; -import { RunContext } from "../../../agent/types"; -import { secretManager } from "../../../common/secrets"; -import { generateTrace } from "../../../agent/helper"; -import { isLangfuseEnabled } from "../../../utils/ee"; +import { processWebhook } from "./processor"; const router = express.Router(); +// This is just an in-memory store for webhook IDs +// TODO: need to use redis or something similar +const webhookIds: string[] = []; + router.post( "/", checkPagerDutySignature, checkWebhookSecret, checkAlertsQuota, - catchAsync(async (req: Request, res: Response) => { - const { organization } = req.webhook!; - const organizationName = organization.name; - const organizationId = String(organization._id); - - const { event } = req.body as PagerDutyWebhookEvent; - - const integrations = (await integrationModel - .get({ - organization: organization._id, - }) - .populate("vendor")) as IIntegration[]; - - // Get integrations to Slack and PagerDuty - let slackIntegration = integrations.find( - (integration) => integration.vendor.name === "Slack", - ) as SlackIntegration; - const pagerdutyIntegration = integrations.find( - (integration) => integration.vendor.name === "PagerDuty", - ) as PagerDutyIntegration; - - if (!slackIntegration) { - throw new AppError("Slack integration was not found", 500); - } else if (!pagerdutyIntegration) { - throw new AppError("PagerDuty integration was not found", 500); - } - - slackIntegration = ( - await secretManager.populateCredentials([slackIntegration]) - )[0] as SlackIntegration; - - // const { channel_id: channelId } = - // slackIntegration.metadata.incoming_webhook; - // TODO: Fix this hardcoded channel ID. It's "#critical-alerts" - const channelId = "C0706NWR3D5"; - - const { access_token: slackToken } = slackIntegration.credentials; - const slackClient = new SlackClient(slackToken); - - const prompt = await parseAlertToPrompt( - event.data.id, - "PagerDuty", - String(organization._id), - ); - - const messages = await slackClient.getChannelHistoryGracefully(channelId); - const pdMessage = messages?.find((message) => - message.text?.includes(event.data.id), - ); - if (!pdMessage) { - throw new AppError("Could not find Slack message", 500); - } - - await postInitialStatus(slackToken, channelId, pdMessage.ts!); - - const context: RunContext = { - organizationName, - organizationId, - env: process.env.NODE_ENV as string, - eventId: event.data.id, - context: "trigger-pagerduty", - }; - if (isLangfuseEnabled()) { - const trace = generateTrace({ ...context }); - context.trace = trace; + async (req: Request, res: Response) => { + // Acknowledge the webhook + res.status(202).send("ok"); + + // PagerDuty sends a webhook ID with every event that can be used to deduplicate events + // https://developer.pagerduty.com/docs/ZG9jOjExMDI5NTkx-behavior#at-least-once-delivery + const webhookId = req.headers["x-webhook-id"] as string; + if (webhookIds.includes(webhookId)) { + console.log("Webhook already processed"); + return; } try { - const { answer, answerContext } = await runAgent({ - prompt, - template: investigationTemplate, - model: chatModel, - integrations, - context, - }); - - const traceId = answerContext.getTraceId()!; - const traceURL = answerContext.getTraceURL()!; - const observationId = answerContext.getObservationId()!; - const event: SystemEvent = { - type: EventType.answer_created, - entityId: String(organization._id), - payload: { - organizationId, - organizationName, - env: process.env.NODE_ENV as string, - context: "trigger-pagerduty", - traceId, - observationId, - traceURL, - }, - }; - if (!isLangfuseEnabled()) { - event.payload.text = answer; - event.payload.prompt = prompt; - } - events.publish(event); - - // Post a reply to the thread - const metadata: MessageMetadata = { - event_type: event.type, - event_payload: event.payload, - }; - - const response = await slackClient.postReply({ - channelId, - ts: pdMessage?.ts as string, - text: answer, - metadata, - }); - - const { ok, ts } = response; - if (ok) { - await slackClient.addFeedbackReactions(channelId, ts!); - } - return res.status(200).send("ok"); - // eslint-disable-next-line @typescript-eslint/no-explicit-any - } catch (error: any) { - throw new AppError(error.message, 500, ErrorCode.AGENT_RUN_FAILED); + await processWebhook(req.webhook!, req.body); + } catch (error) { + console.log("Could not process webhook event"); + console.error(error); } - }), + }, ); export { router }; diff --git a/services/api/src/scripts/analyze.ts b/services/api/src/scripts/analyze.ts new file mode 100644 index 0000000..96b3a0c --- /dev/null +++ b/services/api/src/scripts/analyze.ts @@ -0,0 +1,19 @@ +import { connectToDB } from "@merlinn/db"; +import { runRCA } from "../services/rca"; +import type { EventSource } from "../types/internal"; + +(async () => { + await connectToDB(process.env.MONGO_URI as string); + + // Get the command line arguments + console.log(process.argv.slice(2)); + const args = process.argv.slice(2); + + const eventId = args[0]; + const organizationId = args[1]; + const eventSource = args[2] as EventSource; + + const response = await runRCA(eventId, eventSource, organizationId); + + console.log(response); +})(); diff --git a/services/api/src/services/alerts/alerts.ts b/services/api/src/services/alerts/alerts.ts index 2bc3d4b..fbb772a 100644 --- a/services/api/src/services/alerts/alerts.ts +++ b/services/api/src/services/alerts/alerts.ts @@ -1,5 +1,3 @@ -import { indexModel } from "@merlinn/db"; -import { getVectorStore } from "../../agent/rag"; import type { AlertEvent, EventSource } from "../../types/internal"; import { parseOpsgenieAlert, parsePagerDutyAlert } from "./parsers"; import { buildPrompt } from "./utils"; @@ -11,44 +9,6 @@ export async function parseAlertToPrompt( ) { const event = await parseAlert(eventId, eventSource, organizationId); - const index = await indexModel.getOne({ - organization: organizationId, - }); - - if (index) { - const vectorStore = getVectorStore(index.name, index.type); - - const relatedDocuments = await vectorStore.query({ - query: event.message, - topK: 5, - metadata: { source: eventSource }, - }); - const generalDocuments = await vectorStore.query({ - query: event.message, - topK: 5, - }); - - const context = ` - ${ - relatedDocuments.length && - ` - Context: - Related incidents/alerts from history: - ${relatedDocuments.map((doc) => doc.text).join("\n")} - ` - } - - ${ - generalDocuments.length && - ` - General information about the incident/alert: - ${generalDocuments.map((doc) => doc.text).join("\n")} - ` - } - `; - return buildPrompt(event, context); - } - return buildPrompt(event); } diff --git a/services/api/src/services/rca/index.ts b/services/api/src/services/rca/index.ts new file mode 100644 index 0000000..d4d7581 --- /dev/null +++ b/services/api/src/services/rca/index.ts @@ -0,0 +1,227 @@ +import { parseAlert } from "../alerts"; +import type { EventSource } from "../../types/internal"; +import { + generateQueriesPrompt, + investigationLeanTemplate, + // investigationTemplate, + verifyDocumentPrompt, +} from "../../agent/prompts"; +import { chatModel } from "../../agent/model"; +import { getVectorStore, nodesToText } from "../../agent/rag"; +import type { Document } from "../../agent/rag/types"; +import { buildPrompt } from "../alerts/utils"; +import { + VendorName, + indexModel, + integrationModel, + organizationModel, +} from "@merlinn/db"; +import type { IIndex, IIntegration } from "@merlinn/db"; +import { JsonOutputParser } from "langchain/schema/output_parser"; +import { secretManager } from "../../common/secrets"; +// import { createToolsForVendor } from "../../agent/tools"; +// import { createAgent } from "../../agent/agent"; +import { RunContext } from "../../agent/types"; +import { ToolLoader } from "../../agent/tools/types"; +import { toolLoaders as coralogixToolLoaders } from "../../agent/tools/coralogix"; +import { getLogClusters } from "./logs"; +import { Timeframe } from "../../utils/dates"; + +async function generateQueries( + incidentText: string, + nQueries: number = 3, +): Promise { + const queriesPrompt = await generateQueriesPrompt.format({ + incident: incidentText, + nQueries, + }); + const parser = new JsonOutputParser(); + try { + const { content } = await chatModel.invoke(queriesPrompt); + const { queries } = await parser.parse(content as string); + if (!queries || queries.length === 0) { + throw new Error("No queries generated"); + } + return queries; + } catch (error) { + console.error("Error generating queries", error); + throw error; + } +} + +async function verifyDocument( + incidentText: string, + document: string, +): Promise { + const queriesPrompt = await verifyDocumentPrompt.format({ + incident: incidentText, + document, + }); + try { + const { content } = await chatModel.invoke(queriesPrompt); + const answer = (content as string).toLowerCase(); + + // Since we ask the LLM to just generate true or false, we can just parse the response + return JSON.parse(answer); + } catch (error) { + console.error("Error parsing the response", error); + throw error; + } +} + +// This step is used as a reflectio step, to remove noise +async function filterDocuments( + incidentText: string, + documents: Document[], +): Promise { + const verifications = await Promise.all( + documents.map((doc) => verifyDocument(incidentText, doc.text)), + ); + return documents.filter((_, index) => verifications[index]); +} + +async function runQueries( + index: IIndex, + queries: string[], +): Promise { + const vectorStore = getVectorStore(index.name, index.type); + + const documents = ( + await Promise.all( + queries.map(async (query) => vectorStore.query({ query, topK: 3 })), + ) + ).reduce((acc, val) => acc.concat(val), []); + + return documents; +} + +async function summarize( + incidentText: string, + contextText: string, + additionalInfoText?: string, +): Promise { + const investigationPrompt = await investigationLeanTemplate.format({ + incident: incidentText, + context: contextText, + additionalInfo: additionalInfoText || "no additional information", + }); + const { content } = await chatModel.invoke(investigationPrompt); + return content as string; +} + +async function analyzeLogs( + incidentText: string, + integrations: IIntegration[], + context: RunContext, + timeframe: Timeframe = Timeframe.Last24Hours, +): Promise { + // TODO: extract this array to a constant maybe or introduce a "type" field + // to the integrations + // TODO: in the future, we need to add more vendors to here to support more + // log vendors + const logVendorToolLoaders: { + [key in VendorName]?: ToolLoader[]; + } = { + [VendorName.Coralogix]: coralogixToolLoaders as ToolLoader[], + }; + + const logVendor = integrations.find( + (integration) => logVendorToolLoaders[integration.vendor.name], + ); + if (!logVendor) { + return; + } + + const clusters = await getLogClusters(logVendor, timeframe); + const excludedLevels = ["DEBUG", "INFO", "Information"]; + const formattedClusters = clusters + .filter((cluster) => !excludedLevels.includes(cluster.Level)) + .map((cluster, index) => { + const { + Level, + EventTemplate, + Occurrences, + Percentage, + ...additionalInfo + } = cluster; + return ` + Cluster: ${index + 1} + Log level: ${Level} + Log template: ${EventTemplate} + Occurrences: ${Occurrences} + Percentage: ${Percentage} + Addtitonal Cluster Info: ${JSON.stringify(additionalInfo, null, 2)} + `; + }) + .join("\n----------------\n"); + + // Branch 3 - combine both branches + const finalLogAnalysis = ` + Log aggregation/cluster analysis: + ${formattedClusters} + `; + + return finalLogAnalysis; +} + +export async function runRCA( + eventId: string, + eventSource: EventSource, + organizationId: string, +) { + const organization = await organizationModel.getOneById(organizationId); + if (!organization) { + throw new Error("Organization not found"); + } + + const index = await indexModel.getOne({ + organization: organizationId, + }); + if (!index) { + throw new Error("Knowledge base is not set up. Analysis cannot be done."); + } + + const integrations = (await integrationModel + .get({ + organization: organizationId, + }) + .populate("vendor")) as IIntegration[]; + if (!integrations || integrations.length === 0) { + throw new Error("No integrations found"); + } + + const populatedIntegrations = + await secretManager.populateCredentials(integrations); + + const context: RunContext = { + organizationName: organization.name, + organizationId: organizationId, + env: process.env.NODE_ENV as string, + eventId, + context: `trigger-${eventSource}`, + }; + + const event = await parseAlert(eventId, eventSource, organizationId); + const incidentText = buildPrompt(event); + + // Phase 1 - Information retrieval phase from Vector DB + const queries = await generateQueries(incidentText); + const documents = await runQueries(index, queries); + const filteredDocuments = await filterDocuments(incidentText, documents); + + const topDocuments = filteredDocuments + .sort((a, b) => b.score - a.score) + .slice(0, 3); + const contextText = nodesToText(topDocuments); + + // Phase 2 - Fetch information from logs + const logsText = await analyzeLogs( + incidentText, + populatedIntegrations, + context, + ); + + // Phase 3 - Summarization phase + const analysis = await summarize(incidentText, contextText, logsText); + return analysis; +} diff --git a/services/api/src/services/rca/logs.ts b/services/api/src/services/rca/logs.ts new file mode 100644 index 0000000..2489e7b --- /dev/null +++ b/services/api/src/services/rca/logs.ts @@ -0,0 +1,105 @@ +import axios from "axios"; +import { CoralogixIntegration, IIntegration } from "@merlinn/db"; +import { Timeframe, getTimestamp, timeframe2values } from "../../utils/dates"; +import { CoralogixClient } from "../../clients/coralogix"; +import { extractLogStructureKeysPrompt } from "../../agent/prompts"; +import { JsonOutputParser } from "langchain/schema/output_parser"; +import { chatModel } from "../../agent/model"; + +interface LogCluster extends Record { + Level: string; + EventId: string; + EventTemplate: string; + Occurrences: number; + Percentage: number; +} + +interface ParseLogsResponse { + clusters: LogCluster[]; +} + +async function extractLogStructuralKeys(logRecords: string[]) { + const queriesPrompt = await extractLogStructureKeysPrompt.format({ + logRecords, + }); + const parser = new JsonOutputParser(); + try { + const { content } = await chatModel.invoke(queriesPrompt); + const { severityKey, messageKey } = await parser.parse(content as string); + if (!severityKey || !messageKey) { + throw new Error("Failed to extract log structure keys"); + } + return { severityKey, messageKey }; + } catch (error) { + console.error("Error generating queries", error); + throw error; + } +} + +export async function getLogClusters( + integration: IIntegration, + timeframe: Timeframe = Timeframe.Last24Hours, +): Promise { + switch (integration.vendor.name) { + case "Coralogix": { + const { logsKey } = (integration as CoralogixIntegration).credentials; + const { region } = (integration as CoralogixIntegration).metadata; + + const [amount, scale] = timeframe2values[timeframe]; + const startDate = getTimestamp({ amount, scale }); + const endDate = new Date().toISOString(); + + const client = new CoralogixClient({ logsKey }, region); + const logs = await client.getRawLogs({ + syntax: "QUERY_SYNTAX_DATAPRIME", + query: "", + startDate, + endDate, + }); + + const parsedLogs = client.parseResult(logs); + const sample = parsedLogs.result.results + .slice(0, 2) + .map((log) => log.userData); + try { + const { messageKey, severityKey } = + await extractLogStructuralKeys(sample); + + const logParserUrl = process.env.LOG_PARSER_URL as string; + const { data } = await axios.post( + `${logParserUrl}/parse/coralogix`, + { logs, severityKey, messageKey }, + ); + + return data.clusters; + } catch (error) { + console.error("Error clustering logs", error); + throw error; + } + } + default: { + throw new Error("Unknown log vendor"); + } + } +} + +// (async () => { +// await connectToDB(process.env.MONGO_URI as string); + +// let integrations = (await integrationModel +// .get() +// .populate("vendor")) as IIntegration[]; +// integrations = await secretManager.populateCredentials(integrations); + +// const logVendor = integrations.find( +// (integration) => integration.vendor.name === "Coralogix", +// ); + +// if (!logVendor) { +// throw new Error("No Coralogix integration found"); +// } + +// const clusters = await getLogClusters(logVendor); + +// console.log(clusters); +// })(); diff --git a/services/api/src/telemetry/posthog.ts b/services/api/src/telemetry/posthog.ts index 91f33e2..d7a6d1b 100644 --- a/services/api/src/telemetry/posthog.ts +++ b/services/api/src/telemetry/posthog.ts @@ -4,13 +4,16 @@ import { PostHog as OriginalPosthog } from "posthog-node"; const POSTHOG_API_KEY = "phc_eifETYrGPaTRYKvlarnFMJj9dNfYzoP5CS46K9nsdif"; const POSTHOG_HOST = "https://us.i.posthog.com"; +// const DEBUG = process.env.NODE_ENV === "development"; +const DEBUG = false; + export class PostHogClient extends OriginalPosthog { constructor() { super(POSTHOG_API_KEY, { host: POSTHOG_HOST, }); - if (process.env.NODE_ENV === "development") { + if (DEBUG) { this.debug(); } } diff --git a/services/api/src/utils/dates.ts b/services/api/src/utils/dates.ts index 96eaa31..64942ff 100644 --- a/services/api/src/utils/dates.ts +++ b/services/api/src/utils/dates.ts @@ -9,6 +9,39 @@ export type Timescale = | "minutes" | "seconds"; +export enum Timeframe { + Last1Minute = "Last 1 minute", + Last2Minutes = "Last 2 minutes", + Last5Minutes = "Last 5 minutes", + Last15Minutes = "Last 15 minutes", + Last30Minutes = "Last 30 minutes", + Last1Hour = "Last 1 hour", + Last2Hours = "Last 2 hours", + Last6Hours = "Last 6 hours", + Last12Hours = "Last 12 hours", + Last24Hours = "Last 24 hours", + Last2Days = "Last 2 days", + Last3Days = "Last 3 days", + Last5Days = "Last 5 days", + Last7Days = "Last 7 days", +} + +export const timeframe2values: Record = { + [Timeframe.Last1Minute]: [1, "minutes"], + [Timeframe.Last2Minutes]: [2, "minutes"], + [Timeframe.Last5Minutes]: [5, "minutes"], + [Timeframe.Last15Minutes]: [15, "minutes"], + [Timeframe.Last30Minutes]: [30, "minutes"], + [Timeframe.Last1Hour]: [1, "hours"], + [Timeframe.Last2Hours]: [2, "hours"], + [Timeframe.Last6Hours]: [6, "hours"], + [Timeframe.Last12Hours]: [12, "hours"], + [Timeframe.Last24Hours]: [24, "hours"], + [Timeframe.Last2Days]: [2, "days"], + [Timeframe.Last3Days]: [3, "days"], + [Timeframe.Last5Days]: [5, "days"], + [Timeframe.Last7Days]: [7, "days"], +}; interface GetTimestampParams { offset?: string | number | Date; amount?: number; diff --git a/services/dashboard/src/components/Connection/integrations/basic/PagerDuty.tsx b/services/dashboard/src/components/Connection/integrations/basic/PagerDuty.tsx index 1bf07f2..a06b476 100644 --- a/services/dashboard/src/components/Connection/integrations/basic/PagerDuty.tsx +++ b/services/dashboard/src/components/Connection/integrations/basic/PagerDuty.tsx @@ -13,6 +13,13 @@ const fieldsConfigurations: FieldConfiguration[] = [ type: "credentials", input: { type: "secret" }, }, + { + key: "slackChannelId", + label: "PagerDuty Slack Channel ID", + subtitle: + "The ID of the Slack channel where PagerDuty incidents are posted.", + type: "settings", + }, ]; export const ConnectPagerDutyIntegration = ({ @@ -30,6 +37,7 @@ export const ConnectPagerDutyIntegration = ({ organization: orgId, metadata: { ...(prev?.body?.metadata || {}) }, credentials: { ...(prev?.body?.credentials || {}) }, + settings: { ...(prev?.body?.settings || {}) }, }; body[type as keyof IntegrationPayload][key] = value; diff --git a/services/log-parser/.gitignore b/services/log-parser/.gitignore new file mode 100644 index 0000000..c36e48a --- /dev/null +++ b/services/log-parser/.gitignore @@ -0,0 +1,167 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +coverage +reports +htmlcov/ +.ruff_cache +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml +.DS_Store + + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +*.jsonl \ No newline at end of file diff --git a/services/log-parser/Dockerfile b/services/log-parser/Dockerfile new file mode 100644 index 0000000..7da7f04 --- /dev/null +++ b/services/log-parser/Dockerfile @@ -0,0 +1,19 @@ +FROM python:3.10.5-slim + +ENV OS_ENV=linux + +WORKDIR /app + +RUN apt-get update \ + && apt-get install --no-install-recommends -y \ + build-essential +RUN pip install --no-cache-dir poetry==1.7.1 + +COPY pyproject.toml poetry.lock ./ +RUN poetry config virtualenvs.create true && \ + poetry config virtualenvs.in-project true && \ + poetry install --no-root --no-dev --no-interaction + +COPY . . + +CMD ["poetry", "run", "python", "src/main.py"] \ No newline at end of file diff --git a/services/log-parser/playground.ipynb b/services/log-parser/playground.ipynb new file mode 100644 index 0000000..dc0623e --- /dev/null +++ b/services/log-parser/playground.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"src\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "\n", + "file_path = 'log_response.jsonl'\n", + "jsonObj = pd.read_json(path_or_buf=file_path, lines=True)\n", + "\n", + "raw_logs = []\n", + "for index, row in jsonObj.iterrows():\n", + " batch = row['result']\n", + "\n", + " \n", + " for result in batch['results']:\n", + " raw_logs.append(result)\n", + "\n", + "df_raw_logs = pd.DataFrame(raw_logs)\n", + "\n", + "metadata_df = df_raw_logs['metadata'].apply(lambda row: {obj['key']: obj.get('value') for obj in row})\n", + "labels_df = df_raw_logs['labels'].apply(lambda row: {obj['key']: obj.get('value') for obj in row})\n", + "userData_df = df_raw_logs['userData'].apply(lambda row: pd.json_normalize(json.loads(row)).to_dict())\n", + "\n", + "# # Create DataFrames from the extracted dictionaries\n", + "userData_df = pd.json_normalize(userData_df)\n", + "metadata_df = pd.json_normalize(metadata_df)\n", + "labels_df = pd.json_normalize(labels_df)\n", + "\n", + "# # Concatenate the new DataFrames with the original DataFrame\n", + "df_logs = pd.concat([df_raw_logs.drop(['metadata', 'labels', 'userData'], axis=1),\n", + " userData_df, metadata_df, labels_df], axis=1)\n", + "\n", + "df_logs.columns = df_logs.columns.str.replace('.0', '')\n", + "df_logs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logs = []\n", + "content_col = \"logRecord.body\"\n", + "level_col = \"logRecord.severityText\"\n", + "\n", + "for index, row in df_logs.iterrows():\n", + " timestamp = row[\"timestamp\"]\n", + " content = row[content_col]\n", + " level = row[level_col]\n", + "\n", + " log = f\"{timestamp} {level}: {content}\"\n", + " logs.append(log)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(df_logs['timestamp'] + \" \" + df_logs[level_col] + \": \" + df_logs[content_col]).to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from analyze import analyze_logs\n", + "\n", + "df_enriched_logs, df_templates = analyze_logs(logs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_templates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_final_logs = df_enriched_logs.merge(df_logs, left_index=True, right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "excluded_columns = [\"EventId\", \"EventTemplate\", \"ParameterList\"]\n", + "included_columns = [col for col in df_final_logs.columns if col not in excluded_columns]\n", + "\n", + "def process_template_group(group): \n", + " total_values = {}\n", + "\n", + " # Collect unique values from each column in the group\n", + " for col in included_columns:\n", + " try:\n", + " values = group[col].unique().tolist()\n", + " nvalues = len(values)\n", + " if nvalues == len(group):\n", + " continue\n", + " if len(values) > 10:\n", + " values = [f\"{nvalues} unique values (too many to display)\"]\n", + " concatenated_values = \", \".join(values)\n", + " if len(concatenated_values) > 50:\n", + " concatenated_values = concatenated_values[:50] + \"... (truncated)\"\n", + " total_values[col] = concatenated_values\n", + " except Exception as e:\n", + " continue\n", + "\n", + " # Calculate occurrences of the group\n", + " total_values[\"occurrences\"] = len(group)\n", + " \n", + " series = pd.Series(total_values)\n", + " df = pd.DataFrame(series).T\n", + " return df\n", + "\n", + "df_enriched_templates = df_final_logs.groupby(\"EventTemplate\").apply(process_template_group)\n", + "df_enriched_templates = df_enriched_templates.reset_index().drop(columns=[\"level_1\", \"Content\"])\n", + "df_enriched_templates['percentage'] = df_enriched_templates['occurrences'] / len(df_final_logs) * 100\n", + "df_enriched_templates = df_enriched_templates.sort_values(\"occurrences\", ascending=False)\n", + "\n", + "# Remove log groups with only one occurrence\n", + "df_enriched_templates = df_enriched_templates[df_enriched_templates[\"occurrences\"] > 1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "records = df_enriched_templates.to_dict(orient=\"records\")\n", + "for record in records:\n", + " keys_to_remove = []\n", + " for key in record.keys():\n", + " is_none = record[key] is None\n", + " is_nan = isinstance(record[key], float) and math.isnan(record[key])\n", + "\n", + " is_invalid = is_none or is_nan\n", + " if is_invalid:\n", + " keys_to_remove.append(key)\n", + " \n", + " for key in keys_to_remove:\n", + " del record[key]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"templates.json\", \"w\") as f:\n", + " json.dump(records, f, indent=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "logparser-hmV3RqGv-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": 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"../../node_modules/nx/schemas/project-schema.json", + "projectType": "application", + "sourceRoot": "./src", + "targets": { + "dev": { + "executor": "nx:run-commands", + "options": { + "parallel": false, + "cwd": "services/log-parser", + "commands": ["poetry run python src/main.py"] + } + }, + "deploy": { + "executor": "nx:run-commands", + "options": { + "parallel": false, + "cwd": "services/log-parser", + "commands": ["npx ts-node ../../tools/scripts/deploy.ts log-parser"] + } + }, + "container": { + "executor": "@nx-tools/nx-container:build", + "defaultConfiguration": "local", + "options": { + "context": "services/log-parser", + // "file": "services/log-parser/Dockerfile", + "engine": "docker", + "platforms": ["linux/amd64"] + }, + "configurations": { + "local": { + "tags": ["log-parser:latest"], + "push": false + }, + "production": { + "push": true, + "tags": ["merlinnco/log-parser:latest"] + } + } + } + }, + "tags": [] +} diff --git a/services/log-parser/pyproject.toml b/services/log-parser/pyproject.toml new file mode 100644 index 0000000..caf28ac --- /dev/null +++ b/services/log-parser/pyproject.toml @@ -0,0 +1,20 @@ +[tool.poetry] +name = "log-parser" +version = "0.1.0" +description = "Log-parser service" +authors = ["david1542 "] + +[tool.poetry.dependencies] +python = "^3.10" +logparser3 = "^1.0.4" +fastapi = "^0.111.1" +pandas = "^2.2.2" +uvicorn = "^0.30.3" + + +[tool.poetry.group.dev.dependencies] +ipykernel = "^6.29.5" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/services/log-parser/src/main.py b/services/log-parser/src/main.py new file mode 100644 index 0000000..70a254b --- /dev/null +++ b/services/log-parser/src/main.py @@ -0,0 +1,26 @@ +import os + +from fastapi import FastAPI +import uvicorn + +# Coralogix +from providers.coralogix.processor import analyze_logs +from providers.coralogix.models import CoralogixParseLogsRequest + +app = FastAPI() + + +@app.post("/parse/coralogix") +def parse_logs(request: CoralogixParseLogsRequest): + clusters = analyze_logs(request.logs, request.severityKey, request.messageKey) + + return { + "clusters": clusters, + } + + +if __name__ == "__main__": + port = int(os.getenv("PORT", 3004)) + reload = os.getenv("RELOAD", "true") == "true" + + uvicorn.run("main:app", port=port, reload=reload, host="0.0.0.0") diff --git a/services/log-parser/src/parsers/drain.py b/services/log-parser/src/parsers/drain.py new file mode 100644 index 0000000..58e72ff --- /dev/null +++ b/services/log-parser/src/parsers/drain.py @@ -0,0 +1,417 @@ +# ========================================================================= +# Copyright (C) 2016-2023 LOGPAI (https://github.com/logpai). +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ========================================================================= + + +from typing import List +import regex as re +import os +import pandas as pd +import hashlib +from datetime import datetime + + +class Logcluster: + def __init__(self, logTemplate="", logIDL=None): + self.logTemplate = logTemplate + if logIDL is None: + logIDL = [] + self.logIDL = logIDL + + +class Node: + def __init__(self, childD=None, depth=0, digitOrtoken=None): + if childD is None: + childD = dict() + self.childD = childD + self.depth = depth + self.digitOrtoken = digitOrtoken + + +class LogParser: + def __init__( + self, + log_format, + save=True, + indir="./", + outdir="./result/", + depth=4, + st=0.4, + maxChild=100, + rex=[], + keep_para=True, + logs: List[str] = None, + ): + """ + Attributes + ---------- + rex : regular expressions used in preprocessing (step1) + path : the input path stores the input log file name + depth : depth of all leaf nodes + st : similarity threshold + maxChild : max number of children of an internal node + logName : the name of the input file containing raw log messages + savePath : the output path stores the file containing structured logs + """ + self.save = save + self.path = indir + self.depth = depth - 2 + self.st = st + self.maxChild = maxChild + self.logName = None + self.savePath = outdir + self.df_log = None + self.log_format = log_format + self.rex = rex + self.keep_para = keep_para + self.logs = logs + + def hasNumbers(self, s): + return any(char.isdigit() for char in s) + + def treeSearch(self, rn, seq): + retLogClust = None + + seqLen = len(seq) + if seqLen not in rn.childD: + return retLogClust + + parentn = rn.childD[seqLen] + + currentDepth = 1 + for token in seq: + if currentDepth >= self.depth or currentDepth > seqLen: + break + + if token in parentn.childD: + parentn = parentn.childD[token] + elif "<*>" in parentn.childD: + parentn = parentn.childD["<*>"] + else: + return retLogClust + currentDepth += 1 + + logClustL = parentn.childD + + retLogClust = self.fastMatch(logClustL, seq) + + return retLogClust + + def addSeqToPrefixTree(self, rn, logClust): + seqLen = len(logClust.logTemplate) + if seqLen not in rn.childD: + firtLayerNode = Node(depth=1, digitOrtoken=seqLen) + rn.childD[seqLen] = firtLayerNode + else: + firtLayerNode = rn.childD[seqLen] + + parentn = firtLayerNode + + currentDepth = 1 + for token in logClust.logTemplate: + # Add current log cluster to the leaf node + if currentDepth >= self.depth or currentDepth > seqLen: + if len(parentn.childD) == 0: + parentn.childD = [logClust] + else: + parentn.childD.append(logClust) + break + + # If token not matched in this layer of existing tree. + if token not in parentn.childD: + if not self.hasNumbers(token): + if "<*>" in parentn.childD: + if len(parentn.childD) < self.maxChild: + newNode = Node(depth=currentDepth + 1, digitOrtoken=token) + parentn.childD[token] = newNode + parentn = newNode + else: + parentn = parentn.childD["<*>"] + else: + if len(parentn.childD) + 1 < self.maxChild: + newNode = Node(depth=currentDepth + 1, digitOrtoken=token) + parentn.childD[token] = newNode + parentn = newNode + elif len(parentn.childD) + 1 == self.maxChild: + newNode = Node(depth=currentDepth + 1, digitOrtoken="<*>") + parentn.childD["<*>"] = newNode + parentn = newNode + else: + parentn = parentn.childD["<*>"] + + else: + if "<*>" not in parentn.childD: + newNode = Node(depth=currentDepth + 1, digitOrtoken="<*>") + parentn.childD["<*>"] = newNode + parentn = newNode + else: + parentn = parentn.childD["<*>"] + + # If the token is matched + else: + parentn = parentn.childD[token] + + currentDepth += 1 + + # seq1 is template + def seqDist(self, seq1, seq2): + assert len(seq1) == len(seq2) + simTokens = 0 + numOfPar = 0 + + for token1, token2 in zip(seq1, seq2): + if token1 == "<*>": + numOfPar += 1 + continue + if token1 == token2: + simTokens += 1 + + retVal = float(simTokens) / len(seq1) + + return retVal, numOfPar + + def fastMatch(self, logClustL, seq): + retLogClust = None + + maxSim = -1 + maxNumOfPara = -1 + maxClust = None + + for logClust in logClustL: + curSim, curNumOfPara = self.seqDist(logClust.logTemplate, seq) + if curSim > maxSim or (curSim == maxSim and curNumOfPara > maxNumOfPara): + maxSim = curSim + maxNumOfPara = curNumOfPara + maxClust = logClust + + if maxSim >= self.st: + retLogClust = maxClust + + return retLogClust + + def getTemplate(self, seq1, seq2): + assert len(seq1) == len(seq2) + retVal = [] + + i = 0 + for word in seq1: + if word == seq2[i]: + retVal.append(word) + else: + retVal.append("<*>") + + i += 1 + + return retVal + + def processResult(self, logClustL): + log_templates = [0] * self.df_log.shape[0] + log_templateids = [0] * self.df_log.shape[0] + df_events = [] + for logClust in logClustL: + template_str = " ".join(logClust.logTemplate) + occurrence = len(logClust.logIDL) + template_id = hashlib.md5(template_str.encode("utf-8")).hexdigest()[0:8] + for logID in logClust.logIDL: + logID -= 1 + log_templates[logID] = template_str + log_templateids[logID] = template_id + df_events.append([template_id, template_str, occurrence]) + + df_event = pd.DataFrame( + df_events, columns=["EventId", "EventTemplate", "Occurrences"] + ) + self.df_log["EventId"] = log_templateids + self.df_log["EventTemplate"] = log_templates + if self.keep_para: + self.df_log["ParameterList"] = self.df_log.apply( + self.get_parameter_list, axis=1 + ) + + # Create new df_event + occ_dict = dict(self.df_log["EventTemplate"].value_counts()) + df_event = pd.DataFrame() + df_event["EventTemplate"] = self.df_log["EventTemplate"].unique() + df_event["EventId"] = df_event["EventTemplate"].map( + lambda x: hashlib.md5(x.encode("utf-8")).hexdigest()[0:8] + ) + df_event["Occurrences"] = df_event["EventTemplate"].map(occ_dict) + df_event["Percentage"] = df_event["Occurrences"] / df_event["Occurrences"].sum() + df_event = df_event.sort_values("Occurrences", ascending=False) + + return self.df_log, df_event + + def outputResult(self, df_log, df_event): + df_log.to_csv( + os.path.join(self.savePath, self.logName + "_structured.csv"), index=False + ) + df_event.to_csv( + os.path.join(self.savePath, self.logName + "_templates.csv"), + index=False, + columns=["EventId", "EventTemplate", "Occurrences"], + ) + + def printTree(self, node, dep): + pStr = "" + for i in range(dep): + pStr += "\t" + + if node.depth == 0: + pStr += "Root" + elif node.depth == 1: + pStr += "<" + str(node.digitOrtoken) + ">" + else: + pStr += node.digitOrtoken + + print(pStr) + + if node.depth == self.depth: + return 1 + for child in node.childD: + self.printTree(node.childD[child], dep + 1) + + def parse(self, logName=None): + if logName is not None: + print("Parsing file: " + os.path.join(self.path, logName)) + self.logName = logName + + start_time = datetime.now() + rootNode = Node() + logCluL = [] + + self.load_data() + + count = 0 + for idx, line in self.df_log.iterrows(): + logID = line["LineId"] + logmessageL = self.preprocess(line["Content"]).strip().split() + matchCluster = self.treeSearch(rootNode, logmessageL) + + # Match no existing log cluster + if matchCluster is None: + newCluster = Logcluster(logTemplate=logmessageL, logIDL=[logID]) + logCluL.append(newCluster) + self.addSeqToPrefixTree(rootNode, newCluster) + + # Add the new log message to the existing cluster + else: + newTemplate = self.getTemplate(logmessageL, matchCluster.logTemplate) + matchCluster.logIDL.append(logID) + if " ".join(newTemplate) != " ".join(matchCluster.logTemplate): + matchCluster.logTemplate = newTemplate + + count += 1 + if count % 1000 == 0 or count == len(self.df_log): + print( + "Processed {0:.1f}% of log lines.".format( + count * 100.0 / len(self.df_log) + ) + ) + + if not os.path.exists(self.savePath): + os.makedirs(self.savePath) + + self.df_log = self.processResult(logCluL) + if self.save: + self.outputResult(logCluL) + + print("Parsing done. [Time taken: {!s}]".format(datetime.now() - start_time)) + + return self.df_log + + def load_data(self): + headers, regex = self.generate_logformat_regex(self.log_format) + if not self.logs: + self.df_log = self.log_file_to_dataframe( + os.path.join(self.path, self.logName), regex, headers, self.log_format + ) + else: + self.df_log = self.log_lines_to_dataframe( + self.logs, regex, headers, self.log_format + ) + + def preprocess(self, line): + for currentRex in self.rex: + line = re.sub(currentRex, "<*>", line) + return line + + def log_file_to_dataframe(self, log_file, regex, headers, logformat): + """Function to transform log file to dataframe""" + log_messages = [] + linecount = 0 + with open(log_file, "r") as fin: + for line in fin.readlines(): + try: + match = regex.search(line.strip()) + message = [match.group(header) for header in headers] + log_messages.append(message) + linecount += 1 + except Exception as e: + print("[Warning] Skip line: " + line) + logdf = pd.DataFrame(log_messages, columns=headers) + logdf.insert(0, "LineId", None) + logdf["LineId"] = [i + 1 for i in range(linecount)] + print("Total lines: ", len(logdf)) + return logdf + + def log_lines_to_dataframe(self, logs, regex, headers, logformat): + """Function to transform log file to dataframe""" + log_messages = [] + linecount = 0 + for line in logs: + try: + match = regex.search(line.strip()) + message = [match.group(header) for header in headers] + log_messages.append(message) + linecount += 1 + except Exception as e: + print("[Warning] Skip line: " + line) + logdf = pd.DataFrame(log_messages, columns=headers) + logdf.insert(0, "LineId", None) + logdf["LineId"] = [i + 1 for i in range(linecount)] + print("Total lines: ", len(logdf)) + return logdf + + def generate_logformat_regex(self, logformat): + """Function to generate regular expression to split log messages""" + headers = [] + splitters = re.split(r"(<[^<>]+>)", logformat) + regex = "" + for k in range(len(splitters)): + if k % 2 == 0: + splitter = re.sub(" +", "\\\s+", splitters[k]) + regex += splitter + else: + header = splitters[k].strip("<").strip(">") + regex += "(?P<%s>.*?)" % header + headers.append(header) + regex = re.compile("^" + regex + "$") + return headers, regex + + def get_parameter_list(self, row): + template_regex = re.sub(r"<.{1,5}>", "<*>", row["EventTemplate"]) + if "<*>" not in template_regex: + return [] + template_regex = re.sub(r"([^A-Za-z0-9])", r"\\\1", template_regex) + template_regex = re.sub(r"\\ +", r"\\s+", template_regex) + template_regex = "^" + template_regex.replace("\<\*\>", "(.*?)") + "$" + parameter_list = re.findall(template_regex, row["Content"]) + parameter_list = parameter_list[0] if parameter_list else () + parameter_list = ( + list(parameter_list) + if isinstance(parameter_list, tuple) + else [parameter_list] + ) + return parameter_list diff --git a/services/log-parser/src/providers/coralogix/models.py b/services/log-parser/src/providers/coralogix/models.py new file mode 100644 index 0000000..2988928 --- /dev/null +++ b/services/log-parser/src/providers/coralogix/models.py @@ -0,0 +1,14 @@ +from typing import List +from pydantic import BaseModel + + +class CoralogixLogRecord(BaseModel): + metadata: List[dict[str, str]] + labels: List[dict[str, str]] + userData: str + + +class CoralogixParseLogsRequest(BaseModel): + logs: str + severityKey: str + messageKey: str diff --git a/services/log-parser/src/providers/coralogix/processor.py b/services/log-parser/src/providers/coralogix/processor.py new file mode 100644 index 0000000..c6ef303 --- /dev/null +++ b/services/log-parser/src/providers/coralogix/processor.py @@ -0,0 +1,140 @@ +import json +import math +import pandas as pd +from typing import List, Tuple, Hashable, Any +from parsers.drain import LogParser +from providers.coralogix.models import CoralogixLogRecord + + +def parse_logs(logs: List[str]) -> Tuple[pd.DataFrame, pd.DataFrame]: + log_format = "