feat: Add hypav2 memory option to OtherBotSettings

src/lib/Setting/Pages/OtherBotSettings.svelte

@@ -211,6 +211,7 @@
     <span class="text-textcolor mt-4">{language.type}</span>
 
     <SelectInput value={
+        $DataBase.hypaMemory ? 'hypav2' :
         $DataBase.supaMemoryType !== 'none' ? 'supaMemory' :
         $DataBase.hanuraiEnable ? 'hanuraiMemory' : 'none'
     } on:change={(v) => {
@@ -222,6 +223,9 @@
         } else if (value === 'hanuraiMemory'){
             $DataBase.supaMemoryType = 'none'
             $DataBase.hanuraiEnable = true
+        } else if (value === 'hypav2') {
+            $DataBase.supaMemoryType = 'hypaV2'
+            $DataBase.hanuraiEnable = false
         } else {
             $DataBase.supaMemoryType = 'none'
             $DataBase.hanuraiEnable = false
@@ -229,6 +233,7 @@
     }}>
         <OptionInput value="none" >None</OptionInput>
         <OptionInput value="supaMemory" >{language.SuperMemory}</OptionInput>
+        <OptionInput value="hypav2" >{language.HypaMemory} V2</OptionInput>
         <OptionInput value="hanuraiMemory" >{language.hanuraiMemory}</OptionInput>
     </SelectInput>
 
@@ -238,6 +243,8 @@
         <div class="flex">
             <Check bind:check={$DataBase.hanuraiSplit} name="Text Spliting"/>
         </div>
+    {:else if $DataBase.supaMemoryType === 'hypav2'}
+        <span class="text-textcolor mt-4">{language.HypaMemory} V2 is Experimental</span>
     {:else if $DataBase.supaMemoryType !== 'none'}
         <span class="text-textcolor mt-4">{language.SuperMemory} {language.model}</span>
         <SelectInput className="mt-2 mb-2" bind:value={$DataBase.supaMemoryType}>

src/ts/process/index.ts

@@ -26,6 +26,7 @@ import { runCharacterJS } from "../plugins/embedscript";
 import { addRerolls } from "./prereroll";
 import { runImageEmbedding } from "./transformers";
 import { hanuraiMemory } from "./memory/hanuraiMemory";
+import { hypaMemoryV2 } from "./memory/hypav2";
 
 export interface OpenAIChat{
     role: 'system'|'user'|'assistant'|'function'
@@ -668,6 +669,19 @@ export async function sendChat(chatProcessIndex = -1,arg:{chatAdditonalTokens?:n
             chats = hn.chats
             currentTokens = hn.tokens
         }
+        else if(db.supaMemoryType === 'hypaV2'){
+            const sp = await hypaMemoryV2(chats, currentTokens, maxContextTokens, currentChat, nowChatroom, tokenizer)
+            if(sp.error){
+                alertError(sp.error)
+                return false
+            }
+            chats = sp.chats
+            currentTokens = sp.currentTokens
+            currentChat.hypaV2Data = sp.memory ?? currentChat.hypaV2Data
+            db.characters[selectedChar].chats[selectedChat].hypaV2Data = currentChat.hypaV2Data
+            console.log(currentChat.hypaV2Data)
+            DataBase.set(db)
+        }
         else{
             const sp = await supaMemory(chats, currentTokens, maxContextTokens, currentChat, nowChatroom, tokenizer, {
                 asHyper: db.hypaMemory

src/ts/process/memory/hypav2.ts

@@ -0,0 +1,201 @@
+import { DataBase, type Chat, type character, type groupChat } from "src/ts/storage/database";
+import type { OpenAIChat } from "..";
+import type { ChatTokenizer } from "src/ts/tokenizer";
+import { get } from "svelte/store";
+import { requestChatData } from "../request";
+import { HypaProcesser } from "./hypamemory";
+
+export interface HypaV2Data{
+    chunks: {
+        text:string
+        targetId:string
+    }[]
+    mainChunks: {
+        text:string
+        targetId:string
+    }[]
+}
+
+
+async function summary(stringlizedChat:string):Promise<{
+    success:boolean
+    data:string
+}>{
+    const promptbody:OpenAIChat[] = [
+        {
+            role: "user",
+            content: stringlizedChat
+        },
+        {
+            role: "system",
+            content: "Summarize this roleplay scene in a coherent narrative format for future reference. Summarize what happened, focusing on events and interactions between them. If someone or something is new or changed, include a brief characterization of them."
+        }
+    ]
+    const da = await requestChatData({
+        formated: promptbody,
+        bias: {},
+        useStreaming: false,
+    }, 'model')
+    if(da.type === 'fail' || da.type === 'streaming' || da.type === 'multiline'){
+        return {
+            data: "Hypamemory HTTP: " + da.result,
+            success: false
+        }
+    }
+    return {
+        data: da.result,
+        success: true
+    }
+}
+
+export async function hypaMemoryV2(
+    chats:OpenAIChat[],
+    currentTokens:number,
+    maxContextTokens:number,
+    room:Chat,
+    char:character|groupChat,
+    tokenizer:ChatTokenizer,
+    arg:{asHyper?:boolean} = {}
+): Promise<{ currentTokens: number; chats: OpenAIChat[]; error?:string; memory?:HypaV2Data;}>{
+
+    const db = get(DataBase)
+
+    const data:HypaV2Data = room.hypaV2Data ?? {
+        chunks:[],
+        mainChunks:[]
+    }
+    
+    //this is for the prompt
+
+    let allocatedTokens = 3000
+    currentTokens += allocatedTokens
+    currentTokens += 50 //this is for the template prompt
+    let mainPrompt = ""
+
+    while(data.mainChunks.length > 0){
+        const chunk = data.mainChunks[0]
+        const ind = chats.findIndex(e => e.memo === chunk.targetId)
+        if(ind === -1){
+            data.mainChunks.shift()
+            continue
+        }
+
+        const removedChats = chats.splice(0, ind)
+        for(const chat of removedChats){
+            currentTokens -= await tokenizer.tokenizeChat(chat)
+        }
+        chats = chats.slice(ind)
+        mainPrompt = chunk.text
+        const mpToken = await tokenizer.tokenizeChat({role:'system', content:mainPrompt})
+        allocatedTokens -= mpToken
+        break
+    }
+
+    while(currentTokens >= maxContextTokens){
+    
+        const idx = (Math.floor(chats.length/2))
+        const targetId = chats[idx].memo
+        const halfData = chats.slice(idx)
+
+        let halfDataTokens = 0
+        for(const chat of halfData){
+            halfDataTokens += await tokenizer.tokenizeChat(chat)
+        }
+
+        const stringlizedChat = halfData.map(e => `${e.role}: ${e.content}`).join('\n')
+
+        const summaryData = await summary(stringlizedChat)
+
+        if(!summaryData.success){
+            return {
+                currentTokens: currentTokens,
+                chats: chats,
+                error: summaryData.data
+            }
+        }
+
+        const summaryDataToken = await tokenizer.tokenizeChat({role:'system', content:summaryData.data})
+        mainPrompt += `\n\n${summaryData.data}`
+        currentTokens -= halfDataTokens
+        allocatedTokens -= summaryDataToken
+
+        data.mainChunks.unshift({
+            text: mainPrompt,
+            targetId: targetId
+        })
+
+        if(allocatedTokens < 1500){
+            const summarizedMp = await summary(mainPrompt)
+            const mpToken = await tokenizer.tokenizeChat({role:'system', content:mainPrompt})
+            const summaryToken = await tokenizer.tokenizeChat({role:'system', content:summarizedMp.data})
+
+            allocatedTokens -= summaryToken
+            allocatedTokens += mpToken
+
+            const splited = mainPrompt.split('\n\n').map(e => e.trim()).filter(e => e.length > 0)
+
+            data.chunks.push(...splited.map(e => ({
+                text: e,
+                targetId: targetId
+            })))
+
+            data.mainChunks[0].text = mainPrompt
+        }
+    }
+    
+    const processer = new HypaProcesser("nomic")
+
+    await processer.addText(data.chunks.map((v) => {
+        return "search_document: " + v.text
+    }))
+
+    let scoredResults:{[key:string]:number} = {}
+    for(let i=0;i<3;i++){
+        const pop = chats[chats.length - i - 1]
+        if(!pop){
+            break
+        }
+        const searched = await processer.similaritySearchScored(`search_query: ${pop.content}`)
+        for(const result of searched){
+            const score = result[1]/(i+1)
+            if(scoredResults[result[0]]){
+                scoredResults[result[0]] += score
+            }else{
+                scoredResults[result[0]] = score
+            }
+        }
+    }
+
+    const scoredArray = Object.entries(scoredResults).sort((a,b) => b[1] - a[1])
+
+    let chunkResultPrompts = ""
+    while(allocatedTokens > 0){
+        const target = scoredArray.shift()
+        if(!target){
+            break
+        }
+        const tokenized = await tokenizer.tokenizeChat({
+            role: 'system',
+            content: target[0].substring(14)
+        })
+        if(tokenized > allocatedTokens){
+            break
+        }
+        chunkResultPrompts += target[0].substring(14) + '\n\n'
+        allocatedTokens -= tokenized
+    }
+
+
+    const fullResult = `<Past Events Summary>${mainPrompt}</Past Events Summary>\n<Past Events Details>${chunkResultPrompts}</Past Events Details>`
+
+    chats.unshift({
+        role: "system",
+        content: fullResult,
+        memo: "supaMemory"
+    })
+    return {
+        currentTokens: currentTokens,
+        chats: chats,
+        memory: data
+    }
+}

src/ts/storage/database.ts

@@ -914,6 +914,7 @@ export interface Chat{
     localLore: loreBook[]
     sdData?:string
     supaMemoryData?:string
+    hypaV2Data?:HypaV2Data
     lastMemory?:string
     suggestMessages?:string[]
     isStreaming?:boolean
@@ -1217,6 +1218,7 @@ import { encode as encodeMsgpack, decode as decodeMsgpack } from "msgpackr";
 import * as fflate from "fflate";
 import type { OnnxModelFiles } from '../process/transformers';
 import type { RisuModule } from '../process/modules';
+import type { HypaV2Data } from '../process/memory/hypav2';
 
 export async function downloadPreset(id:number){
     saveCurrentPreset()