feat: add llama command

src/main/kotlin/cn/rtast/fancybot/commands/lookup/AICommand.kt

@@ -10,24 +10,28 @@ package cn.rtast.fancybot.commands.lookup
 import cn.rtast.fancybot.configManager
 import cn.rtast.fancybot.entity.gpt.ChatCompletionsPayload
 import cn.rtast.fancybot.entity.gpt.ChatCompletionsResponse
+import cn.rtast.fancybot.entity.gpt.LlamaResponse
 import cn.rtast.fancybot.entity.gpt.ModelList
 import cn.rtast.fancybot.util.Http
 import cn.rtast.fancybot.util.str.toJson
 import cn.rtast.rob.entity.GroupMessage
 import cn.rtast.rob.util.BaseCommand
 import cn.rtast.rob.util.ob.MessageChain
+import cn.rtast.rob.util.ob.NodeMessageChain
 import cn.rtast.rob.util.ob.OneBotListener
 
 class AICommand : BaseCommand() {
     override val commandNames = listOf("/ai", "问AI")
 
+    private val openAIModel = configManager.openAIModel
+
     override suspend fun executeGroup(listener: OneBotListener, message: GroupMessage, args: List<String>) {
         if (args.isEmpty()) {
             val msg = MessageChain.Builder()
                 .addAt(message.sender.userId)
                 .addText("发送`/ai [模型] <问题>`即可询问AI哦~")
                 .addNewLine()
-                .addText("不指定模型默认为`moonshot-v1-8k`")
+                .addText("不指定模型默认为从配置文件中读取 >>>${openAIModel}")
                 .addNewLine()
                 .addText("发送`/ai list`可以获取可用的模型列表~")
                 .build()
@@ -51,18 +55,43 @@ class AICommand : BaseCommand() {
             return
         }
 
-        val model = if (args.size == 1) "moonshot-v1-8k" else args.first()
+        val model = if (args.size == 1) openAIModel else args.first()
         val content = if (args.size == 1) args.joinToString(" ") else args.drop(1).joinToString(" ")
         val messages = ChatCompletionsPayload(model, listOf(ChatCompletionsPayload.Message(content)))
         val response = Http.post<ChatCompletionsResponse>(
-            "${configManager.openAIAPIHost}/v1/chat/completions",
-            messages.toJson(),
+            "${configManager.openAIAPIHost}/v1/chat/completions", messages.toJson(),
             mapOf("Authorization" to "Bearer ${configManager.openAIAPIKey}")
         )
+        val nodeMsg = NodeMessageChain.Builder()
         val msg = MessageChain.Builder()
-            .addReply(message.messageId)
             .addText(response.choices.first().message.content)
             .build()
-        listener.sendGroupMessage(message.groupId, msg)
+        nodeMsg.addMessageChain(msg, message.sender.userId)
+        listener.sendGroupForwardMsg(message.groupId, nodeMsg.build())
+    }
+}
+
+class LlamaCommand : BaseCommand() {
+    override val commandNames = listOf("/llama")
+
+    private val llamaURL = configManager.llamaUrl
+    private val llamaModel = configManager.llamaModel
+
+    override suspend fun executeGroup(listener: OneBotListener, message: GroupMessage, args: List<String>) {
+        if (args.isEmpty()) {
+            message.reply("发送`/llama <问题>`即可使用llama模型来回复")
+            return
+        }
+        val prompt = args.joinToString(" ")
+        val payload = ChatCompletionsPayload(llamaModel, listOf(ChatCompletionsPayload.Message(prompt)))
+        val response = Http.post<LlamaResponse>("$llamaURL/api/chat", payload.toJson())
+        val nodeMsg = NodeMessageChain.Builder()
+        val msg = MessageChain.Builder()
+            .addText("AI回复如下:")
+            .addNewLine()
+            .addText(response.message.content)
+            .build()
+        nodeMsg.addMessageChain(msg, message.sender.userId)
+        listener.sendGroupForwardMsg(message.groupId, nodeMsg.build())
     }
 }