/**
 * @license
 * Copyright 2025 Google LLC
 * SPDX-License-Identifier: Apache-2.0
 */
import { createHash } from 'node:crypto';
import { GeminiEventType } from '../core/turn.js';
import { logLoopDetected, logLoopDetectionDisabled, logLlmLoopCheck, } from '../telemetry/loggers.js';
import { LoopDetectedEvent, LoopDetectionDisabledEvent, LoopType, LlmLoopCheckEvent, } from '../telemetry/types.js';
import { isFunctionCall, isFunctionResponse, } from '../utils/messageInspectors.js';
import { debugLogger } from '../utils/debugLogger.js';
const TOOL_CALL_LOOP_THRESHOLD = 5;
const CONTENT_LOOP_THRESHOLD = 10;
const CONTENT_CHUNK_SIZE = 50;
const MAX_HISTORY_LENGTH = 5000;
/**
 * The number of recent conversation turns to include in the history when asking the LLM to check for a loop.
 */
const LLM_LOOP_CHECK_HISTORY_COUNT = 20;
/**
 * The number of turns that must pass in a single prompt before the LLM-based loop check is activated.
 */
const LLM_CHECK_AFTER_TURNS = 30;
/**
 * The default interval, in number of turns, at which the LLM-based loop check is performed.
 * This value is adjusted dynamically based on the LLM's confidence.
 */
const DEFAULT_LLM_CHECK_INTERVAL = 3;
/**
 * The minimum interval for LLM-based loop checks.
 * This is used when the confidence of a loop is high, to check more frequently.
 */
const MIN_LLM_CHECK_INTERVAL = 5;
/**
 * The maximum interval for LLM-based loop checks.
 * This is used when the confidence of a loop is low, to check less frequently.
 */
const MAX_LLM_CHECK_INTERVAL = 15;
/**
 * The confidence threshold above which the LLM is considered to have detected a loop.
 */
const LLM_CONFIDENCE_THRESHOLD = 0.9;
const DOUBLE_CHECK_MODEL_ALIAS = 'loop-detection-double-check';
const LOOP_DETECTION_SYSTEM_PROMPT = `You are a sophisticated AI diagnostic agent specializing in identifying when a conversational AI is stuck in an unproductive state. Your task is to analyze the provided conversation history and determine if the assistant has ceased to make meaningful progress.

An unproductive state is characterized by one or more of the following patterns over the last 5 or more assistant turns:

Repetitive Actions: The assistant repeats the same tool calls or conversational responses a decent number of times. This includes simple loops (e.g., tool_A, tool_A, tool_A) and alternating patterns (e.g., tool_A, tool_B, tool_A, tool_B, ...).

Cognitive Loop: The assistant seems unable to determine the next logical step. It might express confusion, repeatedly ask the same questions, or generate responses that don't logically follow from the previous turns, indicating it's stuck and not advancing the task.

Crucially, differentiate between a true unproductive state and legitimate, incremental progress.
For example, a series of 'tool_A' or 'tool_B' tool calls that make small, distinct changes to the same file (like adding docstrings to functions one by one) is considered forward progress and is NOT a loop. A loop would be repeatedly replacing the same text with the same content, or cycling between a small set of files with no net change.`;
const LOOP_DETECTION_SCHEMA = {
    type: 'object',
    properties: {
        unproductive_state_analysis: {
            type: 'string',
            description: 'Your reasoning on if the conversation is looping without forward progress.',
        },
        unproductive_state_confidence: {
            type: 'number',
            description: 'A number between 0.0 and 1.0 representing your confidence that the conversation is in an unproductive state.',
        },
    },
    required: ['unproductive_state_analysis', 'unproductive_state_confidence'],
};
/**
 * Service for detecting and preventing infinite loops in AI responses.
 * Monitors tool call repetitions and content sentence repetitions.
 */
export class LoopDetectionService {
    config;
    promptId = '';
    // Tool call tracking
    lastToolCallKey = null;
    toolCallRepetitionCount = 0;
    // Content streaming tracking
    streamContentHistory = '';
    contentStats = new Map();
    lastContentIndex = 0;
    loopDetected = false;
    inCodeBlock = false;
    // LLM loop track tracking
    turnsInCurrentPrompt = 0;
    llmCheckInterval = DEFAULT_LLM_CHECK_INTERVAL;
    lastCheckTurn = 0;
    // Session-level disable flag
    disabledForSession = false;
    constructor(config) {
        this.config = config;
    }
    /**
     * Disables loop detection for the current session.
     */
    disableForSession() {
        this.disabledForSession = true;
        logLoopDetectionDisabled(this.config, new LoopDetectionDisabledEvent(this.promptId));
    }
    getToolCallKey(toolCall) {
        const argsString = JSON.stringify(toolCall.args);
        const keyString = `${toolCall.name}:${argsString}`;
        return createHash('sha256').update(keyString).digest('hex');
    }
    /**
     * Processes a stream event and checks for loop conditions.
     * @param event - The stream event to process
     * @returns true if a loop is detected, false otherwise
     */
    addAndCheck(event) {
        if (this.disabledForSession) {
            return false;
        }
        if (this.loopDetected) {
            return this.loopDetected;
        }
        switch (event.type) {
            case GeminiEventType.ToolCallRequest:
                // content chanting only happens in one single stream, reset if there
                // is a tool call in between
                this.resetContentTracking();
                this.loopDetected = this.checkToolCallLoop(event.value);
                break;
            case GeminiEventType.Content:
                this.loopDetected = this.checkContentLoop(event.value);
                break;
            default:
                break;
        }
        return this.loopDetected;
    }
    /**
     * Signals the start of a new turn in the conversation.
     *
     * This method increments the turn counter and, if specific conditions are met,
     * triggers an LLM-based check to detect potential conversation loops. The check
     * is performed periodically based on the `llmCheckInterval`.
     *
     * @param signal - An AbortSignal to allow for cancellation of the asynchronous LLM check.
     * @returns A promise that resolves to `true` if a loop is detected, and `false` otherwise.
     */
    async turnStarted(signal) {
        if (this.disabledForSession) {
            return false;
        }
        this.turnsInCurrentPrompt++;
        if (this.turnsInCurrentPrompt >= LLM_CHECK_AFTER_TURNS &&
            this.turnsInCurrentPrompt - this.lastCheckTurn >= this.llmCheckInterval) {
            this.lastCheckTurn = this.turnsInCurrentPrompt;
            return await this.checkForLoopWithLLM(signal);
        }
        return false;
    }
    checkToolCallLoop(toolCall) {
        const key = this.getToolCallKey(toolCall);
        if (this.lastToolCallKey === key) {
            this.toolCallRepetitionCount++;
        }
        else {
            this.lastToolCallKey = key;
            this.toolCallRepetitionCount = 1;
        }
        if (this.toolCallRepetitionCount >= TOOL_CALL_LOOP_THRESHOLD) {
            logLoopDetected(this.config, new LoopDetectedEvent(LoopType.CONSECUTIVE_IDENTICAL_TOOL_CALLS, this.promptId));
            return true;
        }
        return false;
    }
    /**
     * Detects content loops by analyzing streaming text for repetitive patterns.
     *
     * The algorithm works by:
     * 1. Appending new content to the streaming history
     * 2. Truncating history if it exceeds the maximum length
     * 3. Analyzing content chunks for repetitive patterns using hashing
     * 4. Detecting loops when identical chunks appear frequently within a short distance
     * 5. Disabling loop detection within code blocks to prevent false positives,
     *    as repetitive code structures are common and not necessarily loops.
     */
    checkContentLoop(content) {
        // Different content elements can often contain repetitive syntax that is not indicative of a loop.
        // To avoid false positives, we detect when we encounter different content types and
        // reset tracking to avoid analyzing content that spans across different element boundaries.
        const numFences = (content.match(/```/g) ?? []).length;
        const hasTable = /(^|\n)\s*(\|.*\||[|+-]{3,})/.test(content);
        const hasListItem = /(^|\n)\s*[*-+]\s/.test(content) || /(^|\n)\s*\d+\.\s/.test(content);
        const hasHeading = /(^|\n)#+\s/.test(content);
        const hasBlockquote = /(^|\n)>\s/.test(content);
        const isDivider = /^[+-_=*\u2500-\u257F]+$/.test(content);
        if (numFences ||
            hasTable ||
            hasListItem ||
            hasHeading ||
            hasBlockquote ||
            isDivider) {
            // Reset tracking when different content elements are detected to avoid analyzing content
            // that spans across different element boundaries.
            this.resetContentTracking();
        }
        const wasInCodeBlock = this.inCodeBlock;
        this.inCodeBlock =
            numFences % 2 === 0 ? this.inCodeBlock : !this.inCodeBlock;
        if (wasInCodeBlock || this.inCodeBlock || isDivider) {
            return false;
        }
        this.streamContentHistory += content;
        this.truncateAndUpdate();
        return this.analyzeContentChunksForLoop();
    }
    /**
     * Truncates the content history to prevent unbounded memory growth.
     * When truncating, adjusts all stored indices to maintain their relative positions.
     */
    truncateAndUpdate() {
        if (this.streamContentHistory.length <= MAX_HISTORY_LENGTH) {
            return;
        }
        // Calculate how much content to remove from the beginning
        const truncationAmount = this.streamContentHistory.length - MAX_HISTORY_LENGTH;
        this.streamContentHistory =
            this.streamContentHistory.slice(truncationAmount);
        this.lastContentIndex = Math.max(0, this.lastContentIndex - truncationAmount);
        // Update all stored chunk indices to account for the truncation
        for (const [hash, oldIndices] of this.contentStats.entries()) {
            const adjustedIndices = oldIndices
                .map((index) => index - truncationAmount)
                .filter((index) => index >= 0);
            if (adjustedIndices.length > 0) {
                this.contentStats.set(hash, adjustedIndices);
            }
            else {
                this.contentStats.delete(hash);
            }
        }
    }
    /**
     * Analyzes content in fixed-size chunks to detect repetitive patterns.
     *
     * Uses a sliding window approach:
     * 1. Extract chunks of fixed size (CONTENT_CHUNK_SIZE)
     * 2. Hash each chunk for efficient comparison
     * 3. Track positions where identical chunks appear
     * 4. Detect loops when chunks repeat frequently within a short distance
     */
    analyzeContentChunksForLoop() {
        while (this.hasMoreChunksToProcess()) {
            // Extract current chunk of text
            const currentChunk = this.streamContentHistory.substring(this.lastContentIndex, this.lastContentIndex + CONTENT_CHUNK_SIZE);
            const chunkHash = createHash('sha256').update(currentChunk).digest('hex');
            if (this.isLoopDetectedForChunk(currentChunk, chunkHash)) {
                logLoopDetected(this.config, new LoopDetectedEvent(LoopType.CHANTING_IDENTICAL_SENTENCES, this.promptId));
                return true;
            }
            // Move to next position in the sliding window
            this.lastContentIndex++;
        }
        return false;
    }
    hasMoreChunksToProcess() {
        return (this.lastContentIndex + CONTENT_CHUNK_SIZE <=
            this.streamContentHistory.length);
    }
    /**
     * Determines if a content chunk indicates a loop pattern.
     *
     * Loop detection logic:
     * 1. Check if we've seen this hash before (new chunks are stored for future comparison)
     * 2. Verify actual content matches to prevent hash collisions
     * 3. Track all positions where this chunk appears
     * 4. A loop is detected when the same chunk appears CONTENT_LOOP_THRESHOLD times
     *    within a small average distance (≤ 5 * chunk size)
     */
    isLoopDetectedForChunk(chunk, hash) {
        const existingIndices = this.contentStats.get(hash);
        if (!existingIndices) {
            this.contentStats.set(hash, [this.lastContentIndex]);
            return false;
        }
        if (!this.isActualContentMatch(chunk, existingIndices[0])) {
            return false;
        }
        existingIndices.push(this.lastContentIndex);
        if (existingIndices.length < CONTENT_LOOP_THRESHOLD) {
            return false;
        }
        // Analyze the most recent occurrences to see if they're clustered closely together
        const recentIndices = existingIndices.slice(-CONTENT_LOOP_THRESHOLD);
        const totalDistance = recentIndices[recentIndices.length - 1] - recentIndices[0];
        const averageDistance = totalDistance / (CONTENT_LOOP_THRESHOLD - 1);
        const maxAllowedDistance = CONTENT_CHUNK_SIZE * 5;
        return averageDistance <= maxAllowedDistance;
    }
    /**
     * Verifies that two chunks with the same hash actually contain identical content.
     * This prevents false positives from hash collisions.
     */
    isActualContentMatch(currentChunk, originalIndex) {
        const originalChunk = this.streamContentHistory.substring(originalIndex, originalIndex + CONTENT_CHUNK_SIZE);
        return originalChunk === currentChunk;
    }
    trimRecentHistory(recentHistory) {
        // A function response must be preceded by a function call.
        // Continuously removes dangling function calls from the end of the history
        // until the last turn is not a function call.
        while (recentHistory.length > 0 &&
            isFunctionCall(recentHistory[recentHistory.length - 1])) {
            recentHistory.pop();
        }
        // A function response should follow a function call.
        // Continuously removes leading function responses from the beginning of history
        // until the first turn is not a function response.
        while (recentHistory.length > 0 && isFunctionResponse(recentHistory[0])) {
            recentHistory.shift();
        }
        return recentHistory;
    }
    async checkForLoopWithLLM(signal) {
        const recentHistory = this.config
            .getGeminiClient()
            .getHistory()
            .slice(-LLM_LOOP_CHECK_HISTORY_COUNT);
        const trimmedHistory = this.trimRecentHistory(recentHistory);
        const taskPrompt = `Please analyze the conversation history to determine the possibility that the conversation is stuck in a repetitive, non-productive state. Provide your response in the requested JSON format.`;
        const contents = [
            ...trimmedHistory,
            { role: 'user', parts: [{ text: taskPrompt }] },
        ];
        if (contents.length > 0 && isFunctionCall(contents[0])) {
            contents.unshift({
                role: 'user',
                parts: [{ text: 'Recent conversation history:' }],
            });
        }
        const flashResult = await this.queryLoopDetectionModel('loop-detection', contents, signal);
        if (!flashResult) {
            return false;
        }
        const flashConfidence = flashResult['unproductive_state_confidence'];
        const doubleCheckModelName = this.config.modelConfigService.getResolvedConfig({
            model: DOUBLE_CHECK_MODEL_ALIAS,
        }).model;
        if (flashConfidence < LLM_CONFIDENCE_THRESHOLD) {
            logLlmLoopCheck(this.config, new LlmLoopCheckEvent(this.promptId, flashConfidence, doubleCheckModelName, -1));
            this.updateCheckInterval(flashConfidence);
            return false;
        }
        if (this.config.isInFallbackMode()) {
            const flashModelName = this.config.modelConfigService.getResolvedConfig({
                model: 'loop-detection',
            }).model;
            this.handleConfirmedLoop(flashResult, flashModelName);
            return true;
        }
        // Double check with configured model
        const mainModelResult = await this.queryLoopDetectionModel(DOUBLE_CHECK_MODEL_ALIAS, contents, signal);
        const mainModelConfidence = mainModelResult
            ? mainModelResult['unproductive_state_confidence']
            : 0;
        logLlmLoopCheck(this.config, new LlmLoopCheckEvent(this.promptId, flashConfidence, doubleCheckModelName, mainModelConfidence));
        if (mainModelResult) {
            if (mainModelConfidence >= LLM_CONFIDENCE_THRESHOLD) {
                this.handleConfirmedLoop(mainModelResult, doubleCheckModelName);
                return true;
            }
            else {
                this.updateCheckInterval(mainModelConfidence);
            }
        }
        return false;
    }
    async queryLoopDetectionModel(model, contents, signal) {
        try {
            const result = (await this.config.getBaseLlmClient().generateJson({
                modelConfigKey: { model },
                contents,
                schema: LOOP_DETECTION_SCHEMA,
                systemInstruction: LOOP_DETECTION_SYSTEM_PROMPT,
                abortSignal: signal,
                promptId: this.promptId,
                maxAttempts: 2,
            }));
            if (result &&
                typeof result['unproductive_state_confidence'] === 'number') {
                return result;
            }
            return null;
        }
        catch (e) {
            this.config.getDebugMode() ? debugLogger.warn(e) : debugLogger.debug(e);
            return null;
        }
    }
    handleConfirmedLoop(result, modelName) {
        if (typeof result['unproductive_state_analysis'] === 'string' &&
            result['unproductive_state_analysis']) {
            debugLogger.warn(result['unproductive_state_analysis']);
        }
        logLoopDetected(this.config, new LoopDetectedEvent(LoopType.LLM_DETECTED_LOOP, this.promptId, modelName));
    }
    updateCheckInterval(unproductive_state_confidence) {
        this.llmCheckInterval = Math.round(MIN_LLM_CHECK_INTERVAL +
            (MAX_LLM_CHECK_INTERVAL - MIN_LLM_CHECK_INTERVAL) *
                (1 - unproductive_state_confidence));
    }
    /**
     * Resets all loop detection state.
     */
    reset(promptId) {
        this.promptId = promptId;
        this.resetToolCallCount();
        this.resetContentTracking();
        this.resetLlmCheckTracking();
        this.loopDetected = false;
    }
    resetToolCallCount() {
        this.lastToolCallKey = null;
        this.toolCallRepetitionCount = 0;
    }
    resetContentTracking(resetHistory = true) {
        if (resetHistory) {
            this.streamContentHistory = '';
        }
        this.contentStats.clear();
        this.lastContentIndex = 0;
    }
    resetLlmCheckTracking() {
        this.turnsInCurrentPrompt = 0;
        this.llmCheckInterval = DEFAULT_LLM_CHECK_INTERVAL;
        this.lastCheckTurn = 0;
    }
}
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