mirror of
https://github.com/ikechan8370/chatgpt-plugin.git
synced 2025-12-16 21:37:11 +00:00
fix: memory retrieval bug
This commit is contained in:
ikechan8370
parent
0550e6d492
commit
9a5fe1d610
4 changed files with 170 additions and 81 deletions
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@ -114,6 +114,22 @@ function resetSimpleState (overrides = {}) {
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}
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}
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function sanitiseRawFtsInput (input) {
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if (!input) {
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return ''
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}
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const trimmed = String(input).trim()
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if (!trimmed) {
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return ''
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}
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const replaced = trimmed
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.replace(/["'`]+/g, ' ')
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.replace(/\u3000/g, ' ')
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.replace(/[^\p{L}\p{N}\u4E00-\u9FFF\u3040-\u30FF\uAC00-\uD7AF\u1100-\u11FF\s]+/gu, ' ')
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const collapsed = replaced.replace(/\s+/g, ' ').trim()
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return collapsed || trimmed
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}
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function isSimpleLibraryFile (filename) {
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return /(^libsimple.*\.(so|dylib|dll)$)|(^simple\.(so|dylib|dll)$)/i.test(filename)
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}
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@ -644,6 +660,16 @@ export function getSimpleExtensionState () {
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return { ...simpleExtensionState }
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}
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export function sanitiseFtsQueryInput (query, ftsConfig) {
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if (!query) {
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return ''
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}
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if (ftsConfig?.matchQuery) {
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return String(query).trim()
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}
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return sanitiseRawFtsInput(query)
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}
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export function getMemoryDatabase () {
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if (dbInstance) {
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return dbInstance
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@ -1,4 +1,4 @@
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import { SendMessageOption } from 'chaite'
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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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@ -43,9 +43,40 @@ function formatEntry (entry) {
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return str.length > limit ? str.slice(0, limit) + '…' : str
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}
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function resolveGroupExtractionPrompts () {
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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 ${presetId}:`, err)
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return null
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}
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}
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function resolveGroupExtractionPrompts (presetSendMessageOption) {
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const config = ChatGPTConfig.memory?.group || {}
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const system = config.extractionSystemPrompt || `You are a knowledge extraction assistant that specialises in summarising long-term facts from group chat transcripts.
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const system = config.extractionSystemPrompt || presetSendMessageOption?.systemOverride || `You are a knowledge extraction assistant that specialises in summarising long-term facts from group chat transcripts.
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Read the provided conversation and identify statements that should be stored as long-term knowledge for the group.
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Return a JSON array. Each element must contain:
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{
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@ -79,9 +110,9 @@ function buildExistingMemorySection (existingMemories = []) {
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return `以下是关于用户的已知长期记忆，请在提取新记忆时参考，避免重复已有事实，并在信息变更时更新描述：\n${lines.join('\n')}`
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}
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function resolveUserExtractionPrompts (existingMemories = []) {
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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 || `You are an assistant that extracts long-term personal preferences or persona details about a 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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@ -103,8 +134,16 @@ function buildUserPrompt (messages, template) {
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return template.replace('${messages}', body)
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}
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async function callModel ({ prompt, systemPrompt, model, maxToken = 4096, temperature = 0.2 }) {
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const { client } = await getClientForModel(model)
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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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@ -114,10 +153,11 @@ async function callModel ({ prompt, systemPrompt, model, maxToken = 4096, temper
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}
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]
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}, SendMessageOption.create({
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model,
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// temperature,
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maxToken,
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systemOverride: systemPrompt,
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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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@ -125,44 +165,54 @@ async function callModel ({ prompt, systemPrompt, model, maxToken = 4096, temper
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return collectTextFromResponse(response)
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}
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function resolveGroupExtractionModel () {
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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 ChatGPTConfig.llm?.embeddingModel || ''
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return ''
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}
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function resolveUserExtractionModel () {
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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 ChatGPTConfig.llm?.embeddingModel || ''
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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 model = resolveGroupExtractionModel()
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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()
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logger.debug(`[Memory] start group fact extraction, messages=${messages.length}, model=${model}`)
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const prompts = resolveGroupExtractionPrompts(presetOptions)
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logger.debug(`[Memory] start group fact extraction, messages=${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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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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@ -184,18 +234,22 @@ 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 model = resolveUserExtractionModel()
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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)
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logger.debug(`[Memory] start user memory extraction, snippets=${messages.length}, existing=${existingMemories.length}, model=${model}`)
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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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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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@ -1,4 +1,4 @@
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import { getMemoryDatabase, getVectorDimension, getGroupMemoryFtsConfig, resetVectorTableDimension } from './database.js'
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import { getMemoryDatabase, getVectorDimension, getGroupMemoryFtsConfig, resetVectorTableDimension, sanitiseFtsQueryInput } from './database.js'
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import ChatGPTConfig from '../../config/config.js'
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import { embedTexts } from '../chaite/vectorizer.js'
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@ -342,11 +342,13 @@ export class GroupMemoryStore {
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if (!queryText || !queryText.trim()) {
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return []
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}
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const trimmedQuery = queryText.trim()
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const originalQuery = queryText.trim()
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const ftsConfig = getGroupMemoryFtsConfig()
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const matchExpression = ftsConfig.matchQuery ? `${ftsConfig.matchQuery}(?)` : '?'
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const matchQueryParam = sanitiseFtsQueryInput(originalQuery, ftsConfig)
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const results = []
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const seen = new Set()
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if (matchQueryParam) {
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const matchExpression = ftsConfig.matchQuery ? `${ftsConfig.matchQuery}(?)` : '?'
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try {
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const rows = this.db.prepare(`
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SELECT gf.*, bm25(group_facts_fts) AS bm25_score
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@ -356,7 +358,7 @@ export class GroupMemoryStore {
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AND group_facts_fts MATCH ${matchExpression}
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ORDER BY bm25_score ASC
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LIMIT ?
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`).all(groupId, trimmedQuery, limit)
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`).all(groupId, matchQueryParam, limit)
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for (const row of rows) {
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const bm25Threshold = this.bm25Threshold
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if (bm25Threshold) {
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@ -374,6 +376,9 @@ export class GroupMemoryStore {
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} catch (err) {
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logger.warn('Text search failed for group memory:', err)
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}
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} else {
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logger.debug('[Memory] group memory text search skipped MATCH due to empty query after sanitisation')
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}
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if (results.length < limit) {
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try {
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@ -384,7 +389,7 @@ export class GroupMemoryStore {
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AND instr(fact, ?) > 0
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ORDER BY importance DESC, created_at DESC
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LIMIT ?
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`).all(groupId, trimmedQuery, Math.max(limit * 2, limit))
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`).all(groupId, originalQuery, Math.max(limit * 2, limit))
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for (const row of likeRows) {
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if (row && !seen.has(row.id)) {
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results.push(row)
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@ -1,4 +1,4 @@
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import { getMemoryDatabase, getUserMemoryFtsConfig } from './database.js'
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import { getMemoryDatabase, getUserMemoryFtsConfig, sanitiseFtsQueryInput } from './database.js'
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import { md5 } from '../../utils/common.js'
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function normaliseId (value) {
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@ -213,10 +213,14 @@ export class UserMemoryStore {
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const normUserId = normaliseId(userId)
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const normGroupId = normaliseId(groupId)
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const filteredExclude = (excludeIds || []).filter(Boolean)
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const trimmedQuery = queryText.trim()
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const originalQuery = queryText.trim()
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const ftsConfig = getUserMemoryFtsConfig()
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const matchQueryParam = sanitiseFtsQueryInput(originalQuery, ftsConfig)
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const results = []
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const seen = new Set(filteredExclude)
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if (matchQueryParam) {
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const matchExpression = ftsConfig.matchQuery ? `${ftsConfig.matchQuery}(?)` : '?'
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const params = [normUserId]
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const params = [normUserId, matchQueryParam]
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let query = `
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SELECT um.*, bm25(user_memory_fts) AS bm25_score
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FROM user_memory_fts
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@ -224,7 +228,6 @@ export class UserMemoryStore {
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WHERE um.user_id = ?
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AND user_memory_fts MATCH ${matchExpression}
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`
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params.push(trimmedQuery)
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if (normGroupId) {
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query += ' AND (um.group_id = ? OR um.group_id IS NULL)'
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params.push(normGroupId)
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@ -238,17 +241,18 @@ export class UserMemoryStore {
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LIMIT ?
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`
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params.push(limit)
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const results = []
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const seen = new Set(filteredExclude)
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try {
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const ftsRows = this.db.prepare(query).all(...params)
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appendRows(results, ftsRows, seen)
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} catch (err) {
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logger?.warn?.('User memory text search failed:', err)
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}
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} else {
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logger?.debug?.('[Memory] user memory text search skipped MATCH due to empty query after sanitisation')
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}
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if (results.length < limit) {
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const likeParams = [normUserId, trimmedQuery]
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const likeParams = [normUserId, originalQuery]
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let likeQuery = `
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SELECT um.*
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FROM user_memory um
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