feat: Add hypav2 memory option to OtherBotSettings

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
+    }
+}