add chatml related

src/ts/parser.ts

@@ -2480,4 +2480,56 @@ export function applyMarkdownToNode(node: Node) {
             applyMarkdownToNode(child);
         }
     }
+}
+
+export function parseChatML(data:string):OpenAIChat[]|null{
+
+    const starter = '<|im_start|>'
+    const seperator = '<|im_sep|>'
+    const ender = '<|im_end|>'
+    const trimedData = data.trim()
+    if(!trimedData.startsWith(starter)){
+        return null
+    }
+
+    return trimedData.split(starter).filter((f) => f !== '').map((v) => {
+        let role:'system'|'user'|'assistant' = 'user'
+        //default separators
+        if(v.startsWith('user' + seperator)){
+            role = 'user'
+            v = v.substring(4 + seperator.length)
+        }
+        else if(v.startsWith('system' + seperator)){
+            role = 'system'
+            v = v.substring(6 + seperator.length)
+        }
+        else if(v.startsWith('assistant' + seperator)){
+            role = 'system'
+            v = v.substring(9 + seperator.length)
+        }
+        //space/newline separators
+        else if(v.startsWith('user ') || v.startsWith('user\n')){
+            role = 'user'
+            v = v.substring(5)
+        }
+        else if(v.startsWith('system ') || v.startsWith('system\n')){
+            role = 'system'
+            v = v.substring(7)
+        }
+        else if(v.startsWith('assistant ') || v.startsWith('assistant\n')){
+            role = 'assistant'
+            v = v.substring(10)
+        }
+
+        v = v.trim()
+
+        if(v.endsWith(ender)){
+            v = v.substring(0, v.length - ender.length)
+        }
+
+        return {
+            role: role,
+            content: v
+        }
+    })
 }

src/ts/process/triggers.ts

@@ -1,4 +1,4 @@
-import { risuChatParser, risuCommandParser } from "../parser";
+import { parseChatML, risuChatParser, risuCommandParser } from "../parser";
 import { DataBase, type Chat, type character } from "../storage/database";
 import { tokenize } from "../tokenizer";
 import { getModuleTriggers } from "./modules";
@@ -426,41 +426,10 @@ export async function runTrigger(char:character,mode:triggerMode, arg:{
                     }
                     const effectValue = risuChatParser(effect.value,{chara:char})
                     const varName = effect.inputVar
-                    let promptbody:OpenAIChat[] = []
+                    let promptbody:OpenAIChat[] = parseChatML(effectValue)
-                    let currentRole:'user'|'assistant'|'system'
+                    if(!promptbody){
-                    
+                        promptbody = [{role:'user', content:effectValue}]
-                    const splited = effectValue.split('\n')
-
-                    for(let i = 0; i < splited.length; i++){
-                        const line = splited[i]
-                        if(line.startsWith('@@role ')){
-                            const role = line.split(' ')[1]
-                            switch(role){
-                                case 'user':
-                                case 'assistant':
-                                case 'system':
-                                    currentRole = role
-                                    break
-                                default:
-                                    currentRole = 'system'
-                                    break
-                            }
-                            promptbody.push({role: currentRole, content: ''})
-                            continue
-                        }
-                        else if(promptbody.length === 0){
-                            promptbody.push({role: 'system', content: line})
-                        }
-                        else{
-                            promptbody[promptbody.length - 1].content += line
-                        }
                     }
-
-                    promptbody = promptbody.map((e) => {
-                        e.content = e.content.trim()
-                        return e
-                    }).filter((e) => e.content.length > 0)
-
                     const result = await requestChatData({
                         formated: promptbody,
                         bias: {},

src/ts/translator/translator.ts

@@ -4,8 +4,8 @@ import { DataBase, type character, type customscript, type groupChat } from "../
 import { globalFetch, isTauri } from "../storage/globalApi"
 import { alertError } from "../alert"
 import { requestChatData } from "../process/request"
-import { doingChat } from "../process"
+import { doingChat, OpenAIChat } from "../process"
-import { applyMarkdownToNode, type simpleCharacterArgument } from "../parser"
+import { applyMarkdownToNode, parseChatML, type simpleCharacterArgument } from "../parser"
 import { selectedCharID } from "../stores"
 import { getModuleRegexScripts } from "../process/modules"
 import { getNodetextToSentence, sleep } from "../util"
@@ -449,10 +449,15 @@ async function translateLLM(text:string, arg:{to:string}){
         return llmCache.get(text)
     }
     const db = get(DataBase)
+    let formated:OpenAIChat[] = []
     let prompt = db.translatorPrompt || `You are a translator. translate the following html or text into {{slot}}. do not output anything other than the translation.`
-    prompt = prompt.replace('{{slot}}', arg.to)
+    let parsedPrompt = parseChatML(prompt.replaceAll('{{slot}}', arg.to).replaceAll('{{solt::content}}', text))
-    const rq = await requestChatData({
+    if(parsedPrompt){
-        formated: [
+        formated = parsedPrompt
+    }
+    else{
+        prompt = prompt.replaceAll('{{slot}}', arg.to)
+        formated = [
             {
                 'role': 'system',
                 'content': prompt
@@ -461,7 +466,10 @@ async function translateLLM(text:string, arg:{to:string}){
                 'role': 'user',
                 'content': text
             }
-        ],
+        ]
+    }
+    const rq = await requestChatData({
+        formated,
         bias: {},
         useStreaming: false,
         noMultiGen: true,