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试验性的记忆功能 (#812)
* feat: memory basic * fix: chaite ver * fix: update prompt * fix: memory cursor and extract prompt * fix: memory retrieval bug * fix: memory retrieval bug * fix: one more attempt by codex * fix: messages prompt error * fix: one more time by codex * fix: metrics by codex * fix: memory forward * fix: memory show update time
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19 changed files with 4382 additions and 103 deletions
306
models/memory/extractor.js
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306
models/memory/extractor.js
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@ -0,0 +1,306 @@
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import { SendMessageOption, Chaite } from 'chaite'
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import ChatGPTConfig from '../../config/config.js'
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import { getClientForModel } from '../chaite/vectorizer.js'
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function collectTextFromResponse (response) {
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if (!response?.contents) {
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return ''
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}
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return response.contents
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.filter(content => content.type === 'text')
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.map(content => content.text || '')
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.join('\n')
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.trim()
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}
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function parseJSON (text) {
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if (!text) {
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return null
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}
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const trimmed = text.trim()
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const codeBlockMatch = trimmed.match(/^```(?:json)?\s*([\s\S]*?)\s*```$/i)
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const payload = codeBlockMatch ? codeBlockMatch[1] : trimmed
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try {
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return JSON.parse(payload)
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} catch (err) {
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logger.warn('Failed to parse JSON from memory extractor response:', text)
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return null
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}
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}
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function formatEntry (entry) {
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let str = ''
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try {
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if (typeof entry === 'string') {
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str = entry
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} else {
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str = JSON.stringify(entry)
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}
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} catch (err) {
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str = String(entry)
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}
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const limit = 200
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return str.length > limit ? str.slice(0, limit) + '…' : str
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}
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function injectMessagesIntoTemplate (template, body) {
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if (!template || typeof template !== 'string') {
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return body
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}
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const placeholders = ['${messages}', '{messages}', '{{messages}}']
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let result = template
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let replaced = false
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for (const placeholder of placeholders) {
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if (result.includes(placeholder)) {
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result = result.split(placeholder).join(body)
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replaced = true
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}
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}
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if (!replaced) {
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const trimmed = result.trim()
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if (!trimmed) {
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return body
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}
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if (/\n\s*$/.test(result)) {
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return `${result}${body}`
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}
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return `${result}\n${body}`
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}
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return result
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}
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async function resolvePresetSendMessageOption (presetId, scope) {
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if (!presetId) {
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return null
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}
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try {
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const chaite = Chaite.getInstance?.()
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if (!chaite) {
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logger.warn(`[Memory] ${scope} extraction preset ${presetId} configured but Chaite is not initialized`)
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return null
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}
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const presetManager = chaite.getChatPresetManager?.()
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if (!presetManager) {
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logger.warn(`[Memory] ${scope} extraction preset ${presetId} configured but preset manager unavailable`)
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return null
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}
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const preset = await presetManager.getInstance(presetId)
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if (!preset) {
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logger.warn(`[Memory] ${scope} extraction preset ${presetId} not found`)
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return null
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}
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logger.debug(`[Memory] using ${scope} extraction preset ${presetId}`)
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return {
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preset,
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sendMessageOption: JSON.parse(JSON.stringify(preset.sendMessageOption || {}))
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}
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} catch (err) {
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logger.error(`[Memory] failed to load ${scope} extraction preset ${presetId}:`, err)
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return null
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}
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}
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function resolveGroupExtractionPrompts (presetSendMessageOption) {
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const config = ChatGPTConfig.memory?.group || {}
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const system = config.extractionSystemPrompt || presetSendMessageOption?.systemOverride || `You are a knowledge extraction assistant that specialises in summarising long-term facts from group chat transcripts.
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Read the provided conversation and identify statements that should be stored as long-term knowledge for the group.
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Return a JSON array. Each element must contain:
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{
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"fact": 事实内容，必须完整包含事件的各个要素而不能是简单的短语（比如谁参与了事件、做了什么事情、背景时间是什么）（同一件事情尽可能整合为同一条而非拆分，以便利于检索）,
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"topic": 主题关键词，字符串，如 "活动"、"成员信息",
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"importance": 一个介于0和1之间的小数，数值越大表示越重要,
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"source_message_ids": 原始消息ID数组,
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"source_messages": 对应原始消息的简要摘录或合并文本,
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"involved_users": 出现或相关的用户ID数组
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}
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Only include meaningful, verifiable group-specific information that is useful for future conversations. Do not record incomplete information. Do not include general knowledge or unrelated facts. Do not wrap the JSON array in code fences.`
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const userTemplate = config.extractionUserPrompt || `以下是群聊中的一些消息，请根据系统说明提取值得长期记忆的事实，以JSON数组形式返回，不要输出额外说明。
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\${messages}`
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return { system, userTemplate }
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}
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function buildGroupUserPrompt (messages, template) {
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const joined = messages.map(msg => {
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const sender = msg.nickname || msg.user_id || '未知用户'
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return `${sender}: ${msg.text}`
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}).join('\n')
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return injectMessagesIntoTemplate(template, joined)
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}
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function buildExistingMemorySection (existingMemories = []) {
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if (!existingMemories || existingMemories.length === 0) {
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return '当前没有任何已知的长期记忆。'
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}
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const lines = existingMemories.map((item, idx) => `${idx + 1}. ${item}`)
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return `以下是关于用户的已知长期记忆，请在提取新记忆时参考，避免重复已有事实，并在信息变更时更新描述：\n${lines.join('\n')}`
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}
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function resolveUserExtractionPrompts (existingMemories = [], presetSendMessageOption) {
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const config = ChatGPTConfig.memory?.user || {}
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const systemTemplate = config.extractionSystemPrompt || presetSendMessageOption?.systemOverride || `You are an assistant that extracts long-term personal preferences or persona details about a user.
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Given a conversation snippet between the user and the bot, identify durable information such as preferences, nicknames, roles, speaking style, habits, or other facts that remain valid over time.
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Return a JSON array of **strings**, and nothing else, without any other characters including \`\`\` or \`\`\`json. Each string must be a short sentence (in the same language as the conversation) describing one piece of long-term memory. Do not include keys, JSON objects, or additional metadata. Ignore temporary topics or uncertain information.`
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const userTemplate = config.extractionUserPrompt || `下面是用户与机器人的对话，请根据系统提示提取可长期记忆的个人信息。
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\${messages}`
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return {
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system: `${systemTemplate}
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${buildExistingMemorySection(existingMemories)}`,
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userTemplate
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}
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}
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function buildUserPrompt (messages, template) {
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const body = messages.map(msg => {
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const prefix = msg.role === 'assistant' ? '机器人' : (msg.nickname || msg.user_id || '用户')
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return `${prefix}: ${msg.text}`
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}).join('\n')
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return injectMessagesIntoTemplate(template, body)
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}
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async function callModel ({ prompt, systemPrompt, model, maxToken = 4096, temperature = 0.2, sendMessageOption }) {
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const options = sendMessageOption
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? JSON.parse(JSON.stringify(sendMessageOption))
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: {}
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options.model = model || options.model
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if (!options.model) {
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throw new Error('No model available for memory extraction call')
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}
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const resolvedModel = options.model
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const { client } = await getClientForModel(resolvedModel)
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const response = await client.sendMessage({
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role: 'user',
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content: [
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{
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type: 'text',
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text: prompt
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}
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]
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}, SendMessageOption.create({
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...options,
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model: options.model,
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temperature: options.temperature ?? temperature,
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maxToken: options.maxToken ?? maxToken,
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systemOverride: systemPrompt ?? options.systemOverride,
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disableHistoryRead: true,
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disableHistorySave: true,
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stream: false
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}))
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return collectTextFromResponse(response)
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}
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function resolveGroupExtractionModel (presetSendMessageOption) {
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const config = ChatGPTConfig.memory?.group
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if (config?.extractionModel) {
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return config.extractionModel
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}
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if (presetSendMessageOption?.model) {
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return presetSendMessageOption.model
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}
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if (ChatGPTConfig.llm?.defaultModel) {
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return ChatGPTConfig.llm.defaultModel
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}
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return ''
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}
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function resolveUserExtractionModel (presetSendMessageOption) {
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const config = ChatGPTConfig.memory?.user
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if (config?.extractionModel) {
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return config.extractionModel
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}
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if (presetSendMessageOption?.model) {
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return presetSendMessageOption.model
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}
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if (ChatGPTConfig.llm?.defaultModel) {
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return ChatGPTConfig.llm.defaultModel
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}
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return ''
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}
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export async function extractGroupFacts (messages) {
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if (!messages || messages.length === 0) {
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return []
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}
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const groupConfig = ChatGPTConfig.memory?.group || {}
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const presetInfo = await resolvePresetSendMessageOption(groupConfig.extractionPresetId, 'group')
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const presetOptions = presetInfo?.sendMessageOption
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const model = resolveGroupExtractionModel(presetOptions)
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if (!model) {
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logger.warn('No model configured for group memory extraction')
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return []
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}
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try {
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const prompts = resolveGroupExtractionPrompts(presetOptions)
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logger.debug(`[Memory] start group fact extraction, messages=${messages.length}, model=${model}${presetInfo?.preset ? `, preset=${presetInfo.preset.id}` : ''}`)
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const text = await callModel({
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prompt: buildGroupUserPrompt(messages, prompts.userTemplate),
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systemPrompt: prompts.system,
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model,
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sendMessageOption: presetOptions
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})
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const parsed = parseJSON(text)
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if (Array.isArray(parsed)) {
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logger.info(`[Memory] extracted ${parsed.length} group facts`)
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parsed.slice(0, 10).forEach((item, idx) => {
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logger.debug(`[Memory] group fact[${idx}] ${formatEntry(item)}`)
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})
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return parsed
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}
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logger.debug('[Memory] group fact extraction returned non-array content')
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return []
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} catch (err) {
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logger.error('Failed to extract group facts:', err)
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return []
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}
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}
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export async function extractUserMemories (messages, existingMemories = []) {
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if (!messages || messages.length === 0) {
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return []
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}
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const userConfig = ChatGPTConfig.memory?.user || {}
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const presetInfo = await resolvePresetSendMessageOption(userConfig.extractionPresetId, 'user')
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const presetOptions = presetInfo?.sendMessageOption
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const model = resolveUserExtractionModel(presetOptions)
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if (!model) {
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logger.warn('No model configured for user memory extraction')
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return []
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}
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try {
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const prompts = resolveUserExtractionPrompts(existingMemories, presetOptions)
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logger.debug(`[Memory] start user memory extraction, snippets=${messages.length}, existing=${existingMemories.length}, model=${model}${presetInfo?.preset ? `, preset=${presetInfo.preset.id}` : ''}`)
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const text = await callModel({
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prompt: buildUserPrompt(messages, prompts.userTemplate),
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systemPrompt: prompts.system,
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model,
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sendMessageOption: presetOptions
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})
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const parsed = parseJSON(text)
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if (Array.isArray(parsed)) {
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const sentences = parsed.map(item => {
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if (typeof item === 'string') {
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return item.trim()
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}
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if (item && typeof item === 'object') {
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const possible = item.sentence || item.text || item.value || item.fact
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if (possible) {
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return String(possible).trim()
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}
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}
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return ''
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}).filter(Boolean)
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logger.info(`[Memory] extracted ${sentences.length} user memories`)
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sentences.slice(0, 10).forEach((item, idx) => {
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logger.debug(`[Memory] user memory[${idx}] ${formatEntry(item)}`)
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})
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return sentences
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}
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logger.debug('[Memory] user memory extraction returned non-array content')
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return []
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} catch (err) {
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logger.error('Failed to extract user memories:', err)
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return []
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}
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}
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