mirror of
https://github.com/jie65535/JChatGPT.git
synced 2026-09-15 02:56:10 +08:00
profile: add progressive user profiles
Add cache-expiry profile maintenance and contextual injection, reorganize runtime code by responsibility, remove automatic favorability decay, and prepare version 1.15.0.
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package top.jie65535.mirai.llm
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import kotlinx.serialization.json.Json
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import kotlinx.serialization.json.JsonObject
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import kotlinx.serialization.json.jsonObject
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import top.jie65535.mirai.JChatGPT
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import top.jie65535.mirai.config.PluginConfig
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import kotlin.time.Duration.Companion.milliseconds
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object LargeLanguageModels {
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/**
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* 系统提示词
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*/
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var systemPrompt: String = "你是一个乐于助人的助手"
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private set
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/**
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* 一个聊天接入点：封装了请求服务、模型名与温度。
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* 主接入点为列表第 0 项，其余为备用接入点，用于容灾切换。
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*/
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data class ChatEndpoint(
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val service: ModelService,
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val model: String,
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val temperature: Double?,
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/** 唯一标识，用于健康状态跟踪与日志 */
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val label: String,
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)
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data class ProfileEndpoint(
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val service: ModelService,
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val model: String,
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val temperature: Double?,
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)
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/**
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* 聊天接入点列表：index 0 为主接入点，其余按配置顺序为备用接入点。
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*/
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var chatEndpoints: List<ChatEndpoint> = emptyList()
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private set
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/**
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* 主聊天接入点服务（向后兼容旧用法）。
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*/
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val chat: ModelService?
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get() = chatEndpoints.firstOrNull()?.service
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/**
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* 推理模型
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*/
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var reasoning: ModelService? = null
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/**
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* 视觉模型
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*/
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var visual: ModelService? = null
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/** 历史用户画像分析模型。 */
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var profile: ProfileEndpoint? = null
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private set
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/**
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* 接入点健康状态：记录各接入点的冷却截止时间戳（毫秒）。
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* 失败的接入点进入冷却，期间在 [orderedChatEndpoints] 中被排到队尾，
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* 避免每条消息都先卡在故障接入点上白白等一次超时。
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*/
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private val cooldownUntil = HashMap<String, Long>()
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/** 上报某接入点调用失败，使其进入冷却。 */
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fun reportFailure(endpoint: ChatEndpoint) {
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val minutes = PluginConfig.fallbackCooldownMinutes
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// 只有存在备用接入点时冷却才有意义；否则没有可切换的目标，标记冷却反而无益
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if (minutes > 0 && chatEndpoints.size > 1) {
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cooldownUntil[endpoint.label] = System.currentTimeMillis() + minutes * 60_000L
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}
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}
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/** 上报某接入点调用成功，清除其冷却。 */
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fun reportSuccess(endpoint: ChatEndpoint) {
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cooldownUntil.remove(endpoint.label)
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}
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/**
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* 返回按健康度排序的接入点：未冷却的保持配置原顺序在前，冷却中的排到后面
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* （冷却中再按剩余冷却时间升序，优先重试快恢复的）。排序稳定，主接入点健康时始终最先。
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*/
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fun orderedChatEndpoints(): List<ChatEndpoint> {
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if (chatEndpoints.size <= 1) return chatEndpoints
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val now = System.currentTimeMillis()
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return chatEndpoints.sortedBy { ep ->
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val until = cooldownUntil[ep.label] ?: 0L
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if (until > now) until else 0L
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}
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}
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private val json = Json {
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isLenient = true
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ignoreUnknownKeys = true
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}
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private fun parseExtraBody(raw: String): JsonObject? {
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if (raw.isBlank()) return null
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return try {
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json.parseToJsonElement(raw).jsonObject
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} catch (_: Exception) {
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null
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}
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}
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fun reload() {
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val timeout = PluginConfig.timeout.milliseconds
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val firstChunkTimeout = PluginConfig.firstChunkTimeout.milliseconds
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// 初始化聊天接入点（主 + 备用），并重置健康状态
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cooldownUntil.clear()
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val endpoints = mutableListOf<ChatEndpoint>()
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if (PluginConfig.openAiApi.isNotBlank() && PluginConfig.openAiToken.isNotBlank()) {
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endpoints.add(
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ChatEndpoint(
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service = ModelService(
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baseUrl = PluginConfig.openAiApi,
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token = PluginConfig.openAiToken,
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timeout = timeout,
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firstChunkTimeout = firstChunkTimeout,
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extraBody = parseExtraBody(PluginConfig.chatModelExtraBody)
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),
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model = PluginConfig.chatModel,
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temperature = PluginConfig.chatTemperature,
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label = "primary",
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)
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)
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// 备用接入点：留空字段继承主接入点配置
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PluginConfig.chatFallbacks.forEachIndexed { i, fb ->
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val api = fb.api.ifBlank { PluginConfig.openAiApi }
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val token = fb.token.ifBlank { PluginConfig.openAiToken }
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val model = fb.model.ifBlank { PluginConfig.chatModel }
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val extraBody = fb.extraBody.ifBlank { PluginConfig.chatModelExtraBody }
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if (api.isNotBlank() && token.isNotBlank()) {
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endpoints.add(
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ChatEndpoint(
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service = ModelService(
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baseUrl = api,
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token = token,
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timeout = timeout,
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firstChunkTimeout = firstChunkTimeout,
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extraBody = parseExtraBody(extraBody)
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),
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model = model,
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temperature = PluginConfig.chatTemperature,
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label = "fallback$i:$model",
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)
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)
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}
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}
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}
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chatEndpoints = endpoints
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profile = null
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if (PluginConfig.profileEnabled) {
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val api = PluginConfig.profileModelApi.ifBlank { PluginConfig.openAiApi }
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val token = PluginConfig.profileModelToken.ifBlank { PluginConfig.openAiToken }
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val model = PluginConfig.profileModel.ifBlank { PluginConfig.chatModel }
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val extraBody = PluginConfig.profileModelExtraBody.ifBlank { PluginConfig.chatModelExtraBody }
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if (api.isNotBlank() && token.isNotBlank() && model.isNotBlank()) {
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val profileFirstChunk = PluginConfig.profileFirstChunkTimeout.milliseconds
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profile = ProfileEndpoint(
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service = ModelService(
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baseUrl = api,
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token = token,
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timeout = maxOf(timeout, profileFirstChunk),
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firstChunkTimeout = profileFirstChunk,
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extraBody = parseExtraBody(extraBody),
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),
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model = model,
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temperature = PluginConfig.profileModelTemperature ?: PluginConfig.chatTemperature,
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)
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}
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}
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// 初始化推理模型
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if (PluginConfig.reasoningModelApi.isNotBlank() && PluginConfig.reasoningModelToken.isNotBlank()) {
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// 推理模型出首块前常有思考预热，比对话慢，使用单独放宽的首块超时；
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// socket 超时（两次读间隔，等首块时也归它管）不能小于首块预算，否则首块超时形同虚设
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val reasoningFirstChunk = PluginConfig.reasoningFirstChunkTimeout.milliseconds
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reasoning = ModelService(
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baseUrl = PluginConfig.reasoningModelApi,
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token = PluginConfig.reasoningModelToken,
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timeout = maxOf(timeout, reasoningFirstChunk),
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firstChunkTimeout = reasoningFirstChunk,
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extraBody = parseExtraBody(PluginConfig.reasoningModelExtraBody)
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)
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}
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// 初始化视觉模型
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if (PluginConfig.visualModelApi.isNotBlank() && PluginConfig.visualModelToken.isNotBlank()) {
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// 视觉模型需服务端先下载图片再出首块，比对话天然慢，使用单独放宽的首块超时；
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// socket 超时（两次读间隔，等首块时也归它管）不能小于首块预算，否则首块超时形同虚设
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val visualFirstChunk = PluginConfig.visualFirstChunkTimeout.milliseconds
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visual = ModelService(
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baseUrl = PluginConfig.visualModelApi,
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token = PluginConfig.visualModelToken,
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timeout = maxOf(timeout, visualFirstChunk),
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firstChunkTimeout = visualFirstChunk,
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extraBody = parseExtraBody(PluginConfig.visualModelExtraBody)
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)
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}
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// 载入提示词
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if (PluginConfig.promptFile.isNotEmpty()) {
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val file = JChatGPT.resolveConfigFile(PluginConfig.promptFile)
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systemPrompt = if (file.exists()) {
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file.readText()
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} else {
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// 迁移提示词
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file.writeText(PluginConfig.prompt)
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PluginConfig.prompt
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}
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// 空提示词兜底
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if (systemPrompt.isEmpty()) {
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systemPrompt = "你是一个乐于助人的助手"
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}
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}
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}
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}
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