101 lines
4.1 KiB
Kotlin
101 lines
4.1 KiB
Kotlin
/*
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* Copyright © 2024 RTAkland
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* Author: RTAkland
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* Date: 2024/9/15
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*/
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package cn.rtast.fancybot.commands.lookup
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import cn.rtast.fancybot.annotations.CommandDescription
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import cn.rtast.fancybot.configManager
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import cn.rtast.fancybot.entity.gpt.ChatCompletionsPayload
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import cn.rtast.fancybot.entity.gpt.ChatCompletionsResponse
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import cn.rtast.fancybot.entity.gpt.LlamaResponse
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import cn.rtast.fancybot.entity.gpt.ModelList
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import cn.rtast.fancybot.enums.CommandAction
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import cn.rtast.fancybot.util.Http
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import cn.rtast.fancybot.util.file.insertActionRecord
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import cn.rtast.fancybot.util.str.toJson
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import cn.rtast.rob.entity.GroupMessage
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import cn.rtast.rob.onebot.MessageChain
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import cn.rtast.rob.onebot.NodeMessageChain
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import cn.rtast.rob.util.BaseCommand
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@CommandDescription("问AI(GPT)")
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class AICommand : BaseCommand() {
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override val commandNames = listOf("/ai")
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private val openAIModel = configManager.openAIModel
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override suspend fun executeGroup(message: GroupMessage, args: List<String>) {
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if (args.isEmpty()) {
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val msg = MessageChain.Builder()
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.addText("发送`/ai [模型] <问题>`即可询问AI哦~")
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.addNewLine()
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.addText("不指定模型默认为从配置文件中读取 >>>${openAIModel}")
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.addNewLine()
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.addText("发送`/ai list`可以获取可用的模型列表~")
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.build()
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message.reply(msg)
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return
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}
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if (args.first() == "列表" || args.first() == "list") {
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val models = Http.get<ModelList>(
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"${configManager.openAIAPIHost}/v1/models",
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headers = mapOf("Authorization" to "Bearer ${configManager.openAIAPIKey}")
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)
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val modelsString = models.data.joinToString(", ") { it.id }
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val msg = MessageChain.Builder()
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.addText("可用的模型列表如下: ")
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.addNewLine()
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.addText(modelsString)
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.build()
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message.reply(msg)
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return
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}
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val model = if (args.size == 1) openAIModel else args.first()
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val content = if (args.size == 1) args.joinToString(" ") else args.drop(1).joinToString(" ")
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val messages = ChatCompletionsPayload(model, listOf(ChatCompletionsPayload.Message(content)))
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val response = Http.post<ChatCompletionsResponse>(
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"${configManager.openAIAPIHost}/v1/chat/completions", messages.toJson(),
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mapOf("Authorization" to "Bearer ${configManager.openAIAPIKey}")
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)
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val nodeMsg = NodeMessageChain.Builder()
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val msg = MessageChain.Builder()
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.addText(response.choices.first().message.content)
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.build()
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nodeMsg.addMessageChain(msg, configManager.selfId)
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message.action.sendGroupForwardMsg(message.groupId, nodeMsg.build())
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insertActionRecord(CommandAction.AI, message.sender.userId, "$content-$model-GPT")
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}
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}
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@CommandDescription("问AI(LLAMA)")
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class LlamaCommand : BaseCommand() {
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override val commandNames = listOf("/llama")
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private val llamaURL = configManager.llamaUrl
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private val llamaModel = configManager.llamaModel
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override suspend fun executeGroup(message: GroupMessage, args: List<String>) {
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if (args.isEmpty()) {
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message.reply("发送`/llama <问题>`即可使用llama模型来回复")
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return
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}
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val prompt = args.joinToString(" ")
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val payload = ChatCompletionsPayload(llamaModel, listOf(ChatCompletionsPayload.Message(prompt)))
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val response = Http.post<LlamaResponse>("$llamaURL/api/chat", payload.toJson())
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val nodeMsg = NodeMessageChain.Builder()
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val msg = MessageChain.Builder()
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.addText("AI回复如下:")
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.addNewLine()
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.addText(response.message.content)
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.build()
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nodeMsg.addMessageChain(msg, configManager.selfId)
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message.action.sendGroupForwardMsg(message.groupId, nodeMsg.build())
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insertActionRecord(CommandAction.AI, message.sender.userId, "$prompt-LLAMA")
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}
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} |