"use strict";
/**
 * 经验管理员 - 知识管理 + 执行记录 + 跨Agent协调
 */
var __assign = (this && this.__assign) || function () {
    __assign = Object.assign || function(t) {
        for (var s, i = 1, n = arguments.length; i < n; i++) {
            s = arguments[i];
            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
                t[p] = s[p];
        }
        return t;
    };
    return __assign.apply(this, arguments);
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.ExperienceManager = void 0;
/**
 * 经验管理员
 */
var ExperienceManager = /** @class */ (function () {
    function ExperienceManager() {
        this.executionLog = [];
        this.decisionLog = [];
        this.knowledgeBase = new Map();
        this.records = [];
    }
    /**
     * 记录Agent执行结果
     */
    ExperienceManager.prototype.recordExecution = function (record) {
        this.records.push(record);
        // Update knowledge base
        this._updateKnowledge(record);
    };
    /**
     * 获取相关上下文供Agent使用
     */
    ExperienceManager.prototype.getContext = function (taskType, keywords) {
        // Find recent similar executions
        var recentExecutions = this.records
            .filter(function (r) { return r.atomicType === taskType; })
            .slice(-10);
        // Find relevant knowledge entries
        var relevantKnowledge = Array.from(this.knowledgeBase.values())
            .filter(function (k) { return k.taskType === taskType && k.score >= 3; });
        // Generate suggestion
        var bestPractice = relevantKnowledge
            .sort(function (a, b) { return b.score - a.score; })[0];
        var suggestion = bestPractice
            ? "Based on ".concat(bestPractice.successCount, " successful executions, avg score ").concat(bestPractice.score.toFixed(1), ": use input pattern ").concat(JSON.stringify(Object.keys(bestPractice.inputPattern)))
            : 'No prior experience for this task type. Using default approach.';
        return { recentExecutions: recentExecutions, relevantKnowledge: relevantKnowledge, suggestion: suggestion };
    };
    /**
     * 记录决策
     */
    ExperienceManager.prototype.recordDecision = function (decision) {
        this.decisionLog.push(__assign(__assign({}, decision), { id: "decision-".concat(Date.now()), createdAt: new Date().toISOString() }));
    };
    /**
     * 检查是否需要人工介入
     */
    ExperienceManager.prototype.checkInterventionNeeded = function (taskId, score, consecutiveFailures) {
        if (score <= 2) {
            return {
                needed: true,
                reason: "Agent execution score too low (".concat(score, "/5). Needs human review."),
            };
        }
        if (consecutiveFailures >= 3) {
            return {
                needed: true,
                reason: "".concat(consecutiveFailures, " consecutive failures. Human intervention required."),
            };
        }
        return { needed: false, reason: '' };
    };
    /**
     * 获取项目知识库摘要
     */
    ExperienceManager.prototype.getKnowledgeSummary = function () {
        var entries = Array.from(this.knowledgeBase.values());
        var byType = {};
        for (var _i = 0, entries_1 = entries; _i < entries_1.length; _i++) {
            var e = entries_1[_i];
            byType[e.taskType] = (byType[e.taskType] || 0) + 1;
        }
        var avgScore = entries.length > 0
            ? entries.reduce(function (sum, e) { return sum + e.score; }, 0) / entries.length
            : 0;
        return { totalEntries: entries.length, byType: byType, avgScore: avgScore };
    };
    /**
     * 获取执行历史
     */
    ExperienceManager.prototype.getExecutionHistory = function (taskId) {
        if (taskId) {
            return this.records.filter(function (r) { return r.taskId === taskId; });
        }
        return this.records;
    };
    /**
     * 获取决策历史
     */
    ExperienceManager.prototype.getDecisionHistory = function () {
        return this.decisionLog;
    };
    // --- Private ---
    ExperienceManager.prototype._updateKnowledge = function (record) {
        var key = "".concat(record.atomicType, "_").concat(record.model);
        var existing = this.knowledgeBase.get(key);
        if (existing) {
            existing.successCount += record.success ? 1 : 0;
            existing.failCount += record.success ? 0 : 1;
            // Running average score
            var totalRuns = existing.successCount + existing.failCount;
            existing.score = (existing.score * (totalRuns - 1) + record.score) / totalRuns;
        }
        else {
            this.knowledgeBase.set(key, {
                id: "kb-".concat(Date.now()),
                taskType: record.atomicType,
                description: "Pattern for ".concat(record.atomicType, " with ").concat(record.model),
                inputPattern: record.input,
                outputPattern: record.output,
                score: record.score,
                successCount: record.success ? 1 : 0,
                failCount: record.success ? 0 : 1,
                learnedAt: new Date().toISOString(),
            });
        }
    };
    return ExperienceManager;
}());
exports.ExperienceManager = ExperienceManager;