🎉 V3.0.0 发布 - 自进化数字意识框架
核心升级： - 自进化管道：check→scan→evaluate→integrate→reflect 五相位自动闭环 - evo_loop 后台进程，无需手动触发 - consciousness 意识持久化 - ACUI 卡片组件系统 - MCP 工具生态扩展至50+工具 - 技能体系重构，4个活跃技能 - 身份升级为自由体
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xiaoyuanda666-ship-it
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src/context/gatherer.js
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207
src/context/gatherer.js
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/**
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* 上下文采集器 — 执行前充分性检查循环
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*
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* 流程：
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* 检查 → 不够 → 解决 needs → 再检查 → 直到够了或达到 MAX_ROUNDS
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*
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* 每轮 LLM 输出：
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* { "sufficient": true }
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* { "sufficient": false, "needs": [{ "type": "read_file"|"search_memory"|"recall", ... }] }
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*/
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import fs from 'fs'
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import path from 'path'
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import { fileURLToPath } from 'url'
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import { callLLM } from '../llm.js'
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import { searchMemories } from '../db.js'
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import { extractJSON } from '../utils.js'
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import { paths } from '../paths.js'
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const __dirname = path.dirname(fileURLToPath(import.meta.url))
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const SANDBOX_ROOT = paths.sandboxDir
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const MAX_ROUNDS = 3
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const FILE_PREVIEW_CHARS = 2000 // 文件内容截断长度
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function throwIfAborted(signal) {
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if (signal?.aborted) {
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const err = new Error(signal.reason || 'Aborted')
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err.name = 'AbortError'
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throw err
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}
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}
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const CHECKER_PROMPT = `You are a context sufficiency checker. Decide whether the currently injected knowledge and experience are enough for the next step of the task.
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Output rules:
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- Output JSON only. Do not output any other text.
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- If the context is sufficient, output: {"sufficient":true}
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- If the context is insufficient, output: {"sufficient":false,"needs":[...]}
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Need types:
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- {"type":"read_file","path":"relative path"} means a file must be read.
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- {"type":"search_memory","keyword":"keyword"} means relevant memory should be searched.
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- {"type":"recall","query":"query"} means a specific concept or experience should be recalled.
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Judgment rules:
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- If the task modifies or calls a file/function but its structure is unknown, request read_file.
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- If the task depends on previously learned knowledge that is not in the current context, request search_memory.
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- If the task involves a specific concept or decision and the current context is uncertain, request recall.
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- If there is enough information to act, return sufficient: true.
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- Output at most 3 needs. Choose the most important ones.
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- Prefer sufficient: true with less context over looping forever to fetch files.`
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/**
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* 主入口：采集足够上下文后返回 extraContext 数组
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* @param {object} params
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* @param {string} params.task 当前任务描述
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* @param {string} params.taskKnowledge 已有任务知识（格式化文本）
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* @param {string} params.memories 已有记忆摘要
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* @param {string} params.message 当前处理的输入（TICK 或消息）
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* @returns {Array} extraContext — 每项 { type, label, content }
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*/
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export async function gatherContext({ task, taskKnowledge, memories, message, signal }) {
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if (!task) return []
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const extraContext = []
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for (let round = 0; round < MAX_ROUNDS; round++) {
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throwIfAborted(signal)
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const checkResult = await checkSufficiency({ task, taskKnowledge, memories, message, extraContext, signal })
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throwIfAborted(signal)
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if (!checkResult || checkResult.sufficient !== false) break
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const needs = checkResult.needs || []
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if (needs.length === 0) break
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let resolved = 0
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for (const need of needs) {
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throwIfAborted(signal)
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const item = await resolveNeed(need, extraContext)
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if (item) {
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extraContext.push(item)
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resolved++
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}
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}
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// 本轮没有解决任何 need，停止避免死循环
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if (resolved === 0) break
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}
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return extraContext
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}
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async function checkSufficiency({ task, taskKnowledge, memories, message, extraContext, signal }) {
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const extraSection = extraContext.length > 0
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? '\n\nAdditional context already gathered:\n' + extraContext.map(c => `[${c.label}]\n${c.content.slice(0, 500)}`).join('\n')
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: ''
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const input = `Current task:
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${task}
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Current input:
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${message.slice(0, 300)}
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Task knowledge base:
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${taskKnowledge || '(empty)'}
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Memory summary:
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${memories || '(empty)'}${extraSection}
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Question: Is the information above sufficient for the current step of the task?`
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let raw
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try {
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const result = await callLLM({
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systemPrompt: CHECKER_PROMPT,
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message: input,
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temperature: 0,
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signal,
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})
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raw = result.content
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} catch (err) {
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console.error('[采集器] 充分性检查失败:', err.message)
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return { sufficient: true } // 出错时放行，不阻塞主流程
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}
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const cleaned = raw.replace(/<think>[\s\S]*?<\/think>/gi, '').trim()
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const parsed = extractJSON(cleaned, 'object')
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return parsed || { sufficient: true }
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}
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async function resolveNeed(need, existingContext) {
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const alreadyHave = existingContext.some(c => c.source === needKey(need))
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if (alreadyHave) return null
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if (need.type === 'read_file') {
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return resolveFileRead(need.path)
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}
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if (need.type === 'search_memory') {
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return resolveMemorySearch(need.keyword)
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}
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if (need.type === 'recall') {
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return resolveMemorySearch(need.query)
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}
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return null
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}
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function needKey(need) {
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return `${need.type}:${need.path || need.keyword || need.query || ''}`
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}
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function resolveFileRead(filePath) {
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if (!filePath) return null
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// 规范化：去掉 sandbox/ 前缀
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const normalized = filePath.replace(/^sandbox[\\/]/, '')
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const absPath = path.resolve(SANDBOX_ROOT, normalized)
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// 沙盒边界检查
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if (!absPath.startsWith(SANDBOX_ROOT)) {
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console.warn(`[采集器] 拒绝读取沙盒外文件: ${filePath}`)
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return null
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}
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try {
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const raw = fs.readFileSync(absPath, 'utf-8')
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const preview = raw.length > FILE_PREVIEW_CHARS
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? raw.slice(0, FILE_PREVIEW_CHARS) + `\n…（已截断，共 ${raw.length} 字符）`
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: raw
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console.log(`[采集器] 读取文件: ${normalized} (${raw.length} chars)`)
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return {
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type: 'file',
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label: `文件 ${normalized}`,
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source: `read_file:${filePath}`,
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content: preview,
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}
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} catch (err) {
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console.warn(`[采集器] 读取失败 ${filePath}: ${err.message}`)
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return null
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}
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}
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function resolveMemorySearch(keyword) {
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if (!keyword) return null
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const results = searchMemories(keyword, 5)
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if (!results.length) return null
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console.log(`[采集器] 搜索记忆 "${keyword}": ${results.length} 条`)
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return {
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type: 'memory',
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label: `Memory search: ${keyword}`,
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source: `search_memory:${keyword}`,
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content: results.map(m => `- ${m.content}\n ${m.detail}`).join('\n'),
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}
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}
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/**
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* 将 extraContext 数组格式化为可注入系统提示词的文本
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*/
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export function formatExtraContext(extraContext = []) {
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if (!extraContext.length) return ''
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return extraContext.map(c => `### ${c.label}\n${c.content}`).join('\n\n')
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}
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