{"version":3,"file":"ai-model/llm-planning.mjs","sources":["../../../src/ai-model/llm-planning.ts"],"sourcesContent":["import { type TUserPrompt, userPromptToString } from '@/common';\nimport type {\n PlanningAIResponse,\n RawResponsePlanningAIResponse,\n} from '@/types';\nimport { getDebug } from '@midscene/shared/logger';\nimport { assert } from '@midscene/shared/utils';\nimport type { ChatCompletionMessageParam } from 'openai/resources/index';\nimport { buildYamlFlowFromPlans } from '../common';\nimport { planningModelFamilyRequiredForLocateMessage } from './errors';\nimport { systemPromptToTaskPlanning } from './prompt/llm-planning';\nimport {\n extractXMLTag,\n parseMarkFinishedIndexes,\n parseSubGoalsFromXML,\n} from './prompt/util';\nimport { AIResponseParseError, callAI } from './service-caller/index';\nimport type { JsonParser, JsonParserSource } from './service-caller/json';\nimport { prepareModelImage } from './workflows/image-preprocess';\nimport { normalizePlanningActionLocateFields } from './workflows/planning/locate-normalization';\nimport type { PlanOptions } from './workflows/planning/types';\n\nconst debug = getDebug('planning');\nconst warnLog = getDebug('planning', { console: true });\n\nconst noPreviousActionsText =\n 'No previous actions have been executed in this aiAct execution yet. If the instruction asks for actions, choose the first action to execute.';\n\n/**\n * Parse XML response from LLM and convert to RawResponsePlanningAIResponse.\n */\nexport function parseXMLPlanningResponse(\n xmlString: string,\n jsonParser: JsonParser,\n): RawResponsePlanningAIResponse {\n const thought = extractXMLTag(xmlString, 'thought');\n const memory = extractXMLTag(xmlString, 'memory');\n const log = extractXMLTag(xmlString, 'log') || '';\n const error = extractXMLTag(xmlString, 'error');\n const actionType = extractXMLTag(xmlString, 'action-type');\n const actionParamStr = extractXMLTag(xmlString, 'action-param-json');\n\n // Parse tag with success attribute\n const completeGoalRegex =\n /([\\s\\S]*?)<\\/complete>/i;\n const completeGoalMatch = xmlString.match(completeGoalRegex);\n let finalizeMessage: string | undefined;\n let finalizeSuccess: boolean | undefined;\n\n if (completeGoalMatch) {\n finalizeSuccess = completeGoalMatch[1] === 'true';\n finalizeMessage = completeGoalMatch[2]?.trim() || undefined;\n }\n\n // Parse sub-goal related tags\n const updatePlanContent = extractXMLTag(xmlString, 'update-plan-content');\n const markSubGoalDone = extractXMLTag(xmlString, 'mark-sub-goal-done');\n\n const updateSubGoals = updatePlanContent\n ? parseSubGoalsFromXML(updatePlanContent)\n : undefined;\n const markFinishedIndexes = markSubGoalDone\n ? parseMarkFinishedIndexes(markSubGoalDone)\n : undefined;\n\n // Parse action\n let action: any = null;\n if (actionType && actionType.toLowerCase() !== 'null') {\n // Strip any trailing XML tags that LLM might have leaked into the action type\n // e.g. \"KeyboardPress\\n\" -> \"KeyboardPress\"\n const type = actionType.split('<')[0].trim();\n let param: any = undefined;\n\n if (actionParamStr) {\n try {\n // Parse the JSON string in action-param-json\n param = jsonParser(actionParamStr, {\n source: 'planning-action-param',\n preserveStringValueKeys:\n type.toLowerCase() === 'input' ? ['value'] : undefined,\n });\n } catch (e) {\n throw new Error(`Failed to parse action-param-json: ${e}`);\n }\n }\n\n action = {\n type,\n ...(param !== undefined ? { param } : {}),\n };\n }\n\n return {\n ...(thought ? { thought } : {}),\n ...(memory ? { memory } : {}),\n log,\n ...(error ? { error } : {}),\n action,\n ...(finalizeMessage !== undefined ? { finalizeMessage } : {}),\n ...(finalizeSuccess !== undefined ? { finalizeSuccess } : {}),\n ...(updateSubGoals?.length ? { updateSubGoals } : {}),\n ...(markFinishedIndexes?.length ? { markFinishedIndexes } : {}),\n };\n}\n\nexport async function plan(\n userInstruction: TUserPrompt,\n opts: PlanOptions,\n): Promise {\n const { context, conversationHistory } = opts;\n const modelRuntime = opts.modelRuntime;\n const { adapter } = modelRuntime;\n const { shotSize } = context;\n const screenshotBase64 = context.screenshot.base64;\n\n if (opts.includeLocateInPlanning && !modelRuntime.config.modelFamily) {\n throw new Error(\n planningModelFamilyRequiredForLocateMessage(modelRuntime.config.slot),\n );\n }\n\n const locateResultAdapter =\n modelRuntime.config.modelFamily && adapter.locate.kind === 'standard'\n ? adapter.locate.resultAdapter\n : undefined;\n\n // Only enable sub-goals when aiAct is in deep-thinking planning mode.\n const includeSubGoals = opts.deepThink === true;\n\n const systemPrompt = await systemPromptToTaskPlanning({\n actionSpace: opts.actionSpace,\n locatePromptSpec: locateResultAdapter?.promptSpec,\n includeLocateInPlanning: opts.includeLocateInPlanning,\n includeThought: true, // always include thought\n includeSubGoals,\n });\n\n const preparedImage = await prepareModelImage({\n imageBase64: screenshotBase64,\n width: shotSize.width,\n height: shotSize.height,\n policy: adapter.imagePreprocess,\n });\n const imagePayload = preparedImage.imageBase64;\n\n const userInstructionText = userPromptToString(userInstruction);\n const actionContext = opts.actionContext\n ? `${opts.actionContext}\\n`\n : '';\n\n const referenceImageMessages = opts.referenceImageMessages ?? [];\n const instruction: ChatCompletionMessageParam[] = [\n {\n role: 'user',\n content: [\n {\n type: 'text',\n text: `${actionContext}${userInstructionText}`,\n },\n ],\n },\n ...referenceImageMessages,\n ];\n\n let latestFeedbackMessage: ChatCompletionMessageParam;\n\n // Build sub-goal status text to include in the message\n // In planning deep-think mode: show full sub-goals with logs\n // Otherwise: show historical execution logs\n const executionProgressText = includeSubGoals\n ? conversationHistory.subGoalsToText()\n : conversationHistory.historicalLogsToText();\n const executionProgressSection = executionProgressText\n ? `\\n\\n${executionProgressText}`\n : conversationHistory.pendingFeedbackMessage\n ? ''\n : `\\n\\n${noPreviousActionsText}`;\n\n // Build memories text to include in the message\n const memoriesText = conversationHistory.memoriesToText();\n const memoriesSection = memoriesText ? `\\n\\n${memoriesText}` : '';\n\n if (conversationHistory.pendingFeedbackMessage) {\n latestFeedbackMessage = {\n role: 'user',\n content: [\n {\n type: 'text',\n text: `${conversationHistory.pendingFeedbackMessage}. The previous action has been executed, here is the latest screenshot. Please continue according to the instruction.${memoriesSection}${executionProgressSection}`,\n },\n {\n type: 'image_url',\n image_url: {\n url: imagePayload,\n detail: 'high',\n },\n },\n ],\n };\n\n conversationHistory.resetPendingFeedbackMessageIfExists();\n } else {\n latestFeedbackMessage = {\n role: 'user',\n content: [\n {\n type: 'text',\n text: `This is the current screenshot.${memoriesSection}${executionProgressSection}`,\n },\n {\n type: 'image_url',\n image_url: {\n url: imagePayload,\n detail: 'high',\n },\n },\n ],\n };\n }\n conversationHistory.append(latestFeedbackMessage);\n\n // Compress history if it exceeds the threshold to avoid context overflow\n conversationHistory.compressHistory(50, 20);\n\n const historyLog = conversationHistory.snapshot(opts.imagesIncludeCount);\n\n const msgs: ChatCompletionMessageParam[] = [\n { role: 'system', content: systemPrompt },\n ...instruction,\n ...historyLog,\n ];\n\n let {\n content: rawResponse,\n usage,\n reasoning_content,\n rawChoiceMessage,\n } = await callAI(msgs, modelRuntime, {\n abortSignal: opts.abortSignal,\n // Planning with locate results is localization-sensitive. Adapters decide\n // whether this should request original image detail.\n requiresOriginalImageDetail: opts.includeLocateInPlanning,\n });\n\n // Parse XML response to JSON object, retry once on parse failure\n let planFromAI: RawResponsePlanningAIResponse;\n try {\n try {\n planFromAI = parseXMLPlanningResponse(rawResponse, adapter.jsonParser);\n } catch {\n const retry = await callAI(msgs, modelRuntime, {\n abortSignal: opts.abortSignal,\n // Keep retry requests consistent with the initial planning call.\n requiresOriginalImageDetail: opts.includeLocateInPlanning,\n });\n rawResponse = retry.content;\n usage = retry.usage;\n reasoning_content = retry.reasoning_content;\n rawChoiceMessage = retry.rawChoiceMessage;\n planFromAI = parseXMLPlanningResponse(rawResponse, adapter.jsonParser);\n }\n\n if (planFromAI.action && planFromAI.finalizeSuccess !== undefined) {\n warnLog(\n 'Planning response included both an action and ; ignoring output.',\n );\n planFromAI.finalizeMessage = undefined;\n planFromAI.finalizeSuccess = undefined;\n }\n\n const actions = planFromAI.action ? [planFromAI.action] : [];\n let shouldContinuePlanning = true;\n\n // Check if task is completed via tag\n if (planFromAI.finalizeSuccess !== undefined) {\n debug('task completed via tag, stop planning');\n shouldContinuePlanning = false;\n // Mark all sub-goals as finished when goal is completed in planning deep-think mode.\n if (includeSubGoals) {\n conversationHistory.markAllSubGoalsFinished();\n }\n }\n\n const returnValue: PlanningAIResponse = {\n ...planFromAI,\n actions,\n rawResponse,\n rawChoiceMessage,\n usage,\n reasoning_content,\n yamlFlow: buildYamlFlowFromPlans(actions, opts.actionSpace),\n shouldContinuePlanning,\n };\n\n assert(planFromAI, \"can't get plans from AI\");\n\n normalizePlanningActionLocateFields(actions, {\n actionSpace: opts.actionSpace,\n includeLocateInPlanning: opts.includeLocateInPlanning,\n locateResultAdapter,\n locateResultContext: {\n preparedSize: preparedImage.preparedSize,\n contentSize: preparedImage.contentSize,\n },\n });\n\n // Update sub-goals in conversation history only in planning deep-think mode.\n if (includeSubGoals) {\n if (planFromAI.updateSubGoals?.length) {\n conversationHistory.mergeSubGoals(planFromAI.updateSubGoals);\n }\n if (planFromAI.markFinishedIndexes?.length) {\n for (const index of planFromAI.markFinishedIndexes) {\n conversationHistory.markSubGoalFinished(index);\n }\n }\n // Append the planning log to the currently running sub-goal\n if (planFromAI.log) {\n conversationHistory.appendSubGoalLog(planFromAI.log);\n }\n } else {\n // Without planning deep-think mode, accumulate logs as historical execution steps.\n if (planFromAI.log) {\n conversationHistory.appendHistoricalLog(planFromAI.log);\n }\n }\n\n // Append memory to conversation history if present\n if (planFromAI.memory) {\n conversationHistory.appendMemory(planFromAI.memory);\n }\n\n conversationHistory.append({\n role: 'assistant',\n content: [\n {\n type: 'text',\n text: rawResponse,\n },\n ],\n });\n\n return returnValue;\n } catch 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