import { get } from "svelte/store"; import type { OpenAIChat } from "."; import { DataBase, type Chat, type character, type groupChat } from "../storage/database"; import { tokenize } from "../tokenizer"; import { findCharacterbyId } from "../util"; import { requestChatData } from "./request"; export async function supaMemory( chats:OpenAIChat[], currentTokens:number, maxContextTokens:number, room:Chat, char:character|groupChat, chatAdditonalTokens:number ): Promise<{ currentTokens: number; chats: OpenAIChat[]; error?:string; memory?:string;lastId?:string}>{ const db = get(DataBase) currentTokens += 10 if(currentTokens > maxContextTokens){ let coIndex = -1 for(let i=0;i 4){ const splited = room.supaMemoryData.split('\n') const id = splited.splice(0,1)[0] const data = splited.join('\n') let i =0; while(true){ if(chats.length === 0){ return { currentTokens: currentTokens, chats: chats, error: "SupaMemory: chat ID not found" } } if(chats[0].memo === id){ lastId = id break } currentTokens -= (await tokenize(chats[0].content) + chatAdditonalTokens) chats.splice(0, 1) i += 1 } supaMemory = data currentTokens += await tokenize(supaMemory) + chatAdditonalTokens } if(currentTokens < maxContextTokens){ chats.unshift({ role: "system", content: supaMemory }) return { currentTokens: currentTokens, chats: chats } } async function summarize(stringlizedChat:string){ const supaPrompt = db.supaMemoryPrompt === '' ? "[Summarize the ongoing role story, including as many events from the past as possible, using assistant as a narrative helper;do not analyze. include all of the characters' names, statuses, thoughts, relationships, and attire. Be sure to include dialogue exchanges and context by referencing previous statements and reactions. assistant's summary should provide an objective overview of the story while also considering relevant past conversations and events. It must also remove redundancy and unnecessary content from the prompt so that gpt3 and other sublanguage models]\n" : db.supaMemoryPrompt let result = '' if(db.supaMemoryType !== 'subModel'){ const promptbody = stringlizedChat + '\n\n' + supaPrompt + "\n\nOutput:" const da = await fetch("https://api.openai.com/v1/completions",{ headers: { "Content-Type": "application/json", "Authorization": "Bearer " + db.supaMemoryKey }, method: "POST", body: JSON.stringify({ "model": db.supaMemoryType === 'curie' ? "text-curie-001" : "text-davinci-003", "prompt": promptbody, "max_tokens": 600, "temperature": 0 }) }) if(da.status < 200 || da.status >= 300){ return { currentTokens: currentTokens, chats: chats, error: "SupaMemory: HTTP: " + await da.text() } } result = (await da.json()).choices[0].text.trim() } else { const promptbody:OpenAIChat[] = [ { role: "user", content: stringlizedChat }, { role: "system", content: supaPrompt } ] const da = await requestChatData({ formated: promptbody, bias: {} }, 'submodel') if(da.type === 'fail' || da.type === 'streaming'){ return { currentTokens: currentTokens, chats: chats, error: "SupaMemory: HTTP: " + da.result } } result = da.result } return result } while(currentTokens > maxContextTokens){ const beforeToken = currentTokens let maxChunkSize = maxContextTokens > 3500 ? 1200 : Math.floor(maxContextTokens / 3) let summarized = false let chunkSize = 0 let stringlizedChat = '' let spiceLen = 0 while(true){ const cont = chats[spiceLen] if(!cont){ currentTokens = beforeToken stringlizedChat = '' chunkSize = 0 spiceLen = 0 if(summarized){ if(maxChunkSize < 500){ return { currentTokens: currentTokens, chats: chats, error: "Not Enough Tokens" } } maxChunkSize = maxChunkSize * 0.7 } else{ const result = await summarize(supaMemory) if(typeof(result) !== 'string'){ return result } console.log(currentTokens) currentTokens -= await tokenize(supaMemory) currentTokens += await tokenize(result + '\n\n') console.log(currentTokens) supaMemory = result + '\n\n' summarized = true if(currentTokens <= maxContextTokens){ break } } continue } const tokens = await tokenize(cont.content) + chatAdditonalTokens if((chunkSize + tokens) > maxChunkSize){ if(stringlizedChat === ''){ stringlizedChat += `${cont.role === 'assistant' ? char.type === 'group' ? '' : char.name : db.username}: ${cont.content}\n\n` } lastId = cont.memo break } stringlizedChat += `${cont.role === 'assistant' ? char.type === 'group' ? '' : char.name : db.username}: ${cont.content}\n\n` spiceLen += 1 currentTokens -= tokens chunkSize += tokens } chats.splice(0, spiceLen) if(stringlizedChat !== ''){ const result = await summarize(stringlizedChat) if(typeof(result) !== 'string'){ return result } const tokenz = await tokenize(result + '\n\n') + chatAdditonalTokens currentTokens += tokenz supaMemory += result.replace(/\n+/g,'\n') + '\n\n' let SupaMemoryList = supaMemory.split('\n\n') if(SupaMemoryList.length >= 5){ const oldSupaMemory = supaMemory let modifies = [] for(let i=0;i<3;i++){ modifies.push(SupaMemoryList.shift()) } const result = await summarize(supaMemory) if(typeof(result) !== 'string'){ return result } modifies.unshift(result.replace(/\n+/g,'\n')) supaMemory = modifies.join('\n\n') + '\n\n' currentTokens -= await tokenize(oldSupaMemory) currentTokens += await tokenize(supaMemory) } } } chats.unshift({ role: "system", content: supaMemory }) return { currentTokens: currentTokens, chats: chats, memory: lastId + '\n' + supaMemory, lastId: lastId } } return { currentTokens: currentTokens, chats: chats } }