diff --git a/src/ts/process/index.ts b/src/ts/process/index.ts
index ea679c51..4da73d91 100644
--- a/src/ts/process/index.ts
+++ b/src/ts/process/index.ts
@@ -714,7 +714,7 @@ export async function sendChat(chatProcessIndex = -1,arg:{chatAdditonalTokens?:n
currentTokens += await tokenizer.tokenizeChat(chat)
}
- if(nowChatroom.supaMemory && (db.supaMemoryType !== 'none' || db.hanuraiEnable)){
+ if(nowChatroom.supaMemory && (db.supaModelType !== 'none' || db.hanuraiEnable || db.hypav2)){
chatProcessStage.set(2)
if(db.hanuraiEnable){
const hn = await hanuraiMemory(chats, {
@@ -730,9 +730,11 @@ export async function sendChat(chatProcessIndex = -1,arg:{chatAdditonalTokens?:n
chats = hn.chats
currentTokens = hn.tokens
}
- else if(db.supaMemoryType === 'hypaV2'){
+ else if(db.hypav2){ //HypaV2 support needs to be changed like this.
const sp = await hypaMemoryV2(chats, currentTokens, maxContextTokens, currentChat, nowChatroom, tokenizer)
+ console.log("All chats: ", chats)
if(sp.error){
+ console.log(sp)
alertError(sp.error)
return false
}
diff --git a/src/ts/process/memory/hypav2.ts b/src/ts/process/memory/hypav2.ts
index 6ac6d97c..c7295267 100644
--- a/src/ts/process/memory/hypav2.ts
+++ b/src/ts/process/memory/hypav2.ts
@@ -4,209 +4,306 @@ import type { ChatTokenizer } from "src/ts/tokenizer";
import { get } from "svelte/store";
import { requestChatData } from "../request";
import { HypaProcesser } from "./hypamemory";
+import { globalFetch } from "src/ts/storage/globalApi";
+import { runSummarizer } from "../transformers";
+import { last, remove } from "lodash";
-export interface HypaV2Data{
+export interface HypaV2Data {
chunks: {
- text:string
- targetId:string
- }[]
+ text: string;
+ targetId: string;
+ }[];
mainChunks: {
- text:string
- targetId:string
- }[]
+ text: string;
+ targetId: string;
+ }[];
}
+async function summary(stringlizedChat: string): Promise<{ success: boolean; data: string }> {
+ const db = get(DataBase);
+ console.log("Summarizing");
-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,
- noMultiGen: true
- }, 'model')
- if(da.type === 'fail' || da.type === 'streaming' || da.type === 'multiline'){
- return {
- data: "Hypamemory HTTP: " + da.result,
- success: false
+ if (db.supaModelType === 'distilbart') {
+ try {
+ const sum = await runSummarizer(stringlizedChat);
+ return { success: true, data: sum };
+ } catch (error) {
+ return {
+ success: false,
+ data: "SupaMemory: Summarizer: " + `${error}`
+ };
}
}
- return {
- data: da.result,
- success: true
+
+ const supaPrompt = db.supaMemoryPrompt === '' ?
+ "[Summarize the ongoing role story, It must also remove redundancy and unnecessary text and content from the output to reduce tokens for gpt3 and other sublanguage models]\n"
+ : db.supaMemoryPrompt;
+ let result = '';
+
+ if (db.supaModelType !== 'subModel') {
+ const promptbody = stringlizedChat + '\n\n' + supaPrompt + "\n\nOutput:";
+
+ const da = await globalFetch("https://api.openai.com/v1/completions", {
+ headers: {
+ "Content-Type": "application/json",
+ "Authorization": "Bearer " + db.supaMemoryKey
+ },
+ method: "POST",
+ body: {
+ "model": db.supaModelType === 'curie' ? "text-curie-001"
+ : db.supaModelType === 'instruct35' ? 'gpt-3.5-turbo-instruct'
+ : "text-davinci-003",
+ "prompt": promptbody,
+ "max_tokens": 600,
+ "temperature": 0
+ }
+ })
+ console.log("Using openAI instruct 3.5 for SupaMemory");
+
+ try {
+ if (!da.ok) {
+ return {
+ success: false,
+ data: "SupaMemory: HTTP: " + JSON.stringify(da)
+ };
+ }
+
+ result = (await da.data)?.choices[0]?.text?.trim();
+
+ if (!result) {
+ return {
+ success: false,
+ data: "SupaMemory: HTTP: " + JSON.stringify(da)
+ };
+ }
+
+ return { success: true, data: result };
+ } catch (error) {
+ return {
+ success: false,
+ data: "SupaMemory: HTTP: " + error
+ };
+ }
+ } else {
+ const promptbody: OpenAIChat[] = [
+ {
+ role: "user",
+ content: stringlizedChat
+ },
+ {
+ role: "system",
+ content: supaPrompt
+ }
+ ];
+ console.log("Using submodel: ", db.subModel, "for supaMemory model");
+ const da = await requestChatData({
+ formated: promptbody,
+ bias: {},
+ useStreaming: false,
+ noMultiGen: true
+ }, 'submodel');
+ if (da.type === 'fail' || da.type === 'streaming' || da.type === 'multiline') {
+ return {
+ success: false,
+ data: "SupaMemory: HTTP: " + da.result
+ };
+ }
+ result = da.result;
}
+ return { success: true, data: result };
}
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;}>{
+ chats: OpenAIChat[],
+ currentTokens: number,
+ maxContextTokens: number,
+ room: Chat,
+ char: character | groupChat,
+ tokenizer: ChatTokenizer,
+ arg: { asHyper?: boolean, summaryModel?: string, summaryPrompt?: string, hypaModel?: string } = {}
+): Promise<{ currentTokens: number; chats: OpenAIChat[]; error?: string; memory?: HypaV2Data; }> {
- const db = get(DataBase)
+ const db = get(DataBase);
+ const data: HypaV2Data = room.hypaV2Data ?? { chunks: [], mainChunks: [] };
- const data:HypaV2Data = room.hypaV2Data ?? {
- chunks:[],
- mainChunks:[]
- }
-
- //this is for the prompt
+ let allocatedTokens = db.hypaAllocatedTokens;
+ let chunkSize = db.hypaChunkSize;
+ currentTokens += allocatedTokens + 50;
+ let mainPrompt = "";
+ const lastTwoChats = chats.slice(-2);
+ // Error handling for infinite summarization attempts
+ let summarizationFailures = 0;
+ const maxSummarizationFailures = 3;
+ let lastMainChunkTargetId = '';
- let allocatedTokens = db.hypaAllocatedTokens
- let chunkSize = db.hypaChunkSize
- currentTokens += allocatedTokens
- currentTokens += 50 //this is for the template prompt
- let mainPrompt = ""
+ // Ensure correct targetId matching
+ const getValidChatIndex = (targetId: string) => {
+ return chats.findIndex(chat => chat.memo === targetId);
+ };
- 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
+ // Processing mainChunks
+ if (data.mainChunks.length > 0) {
+ const chunk = data.mainChunks[0];
+ const ind = getValidChatIndex(chunk.targetId);
+ if (ind !== -1) {
+ const removedChats = chats.splice(0, ind + 1);
+ console.log("removed chats", removedChats);
+ for (const chat of removedChats) {
+ currentTokens -= await tokenizer.tokenizeChat(chat);
+ }
+ mainPrompt = chunk.text;
+ const mpToken = await tokenizer.tokenizeChat({ role: 'system', content: mainPrompt });
+ allocatedTokens -= mpToken;
}
-
- 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){
-
- let idx = 0
- let targetId = ''
- const halfData:OpenAIChat[] = []
+ // Token management loop
+ while (currentTokens >= maxContextTokens) {
+ let idx = 0;
+ let targetId = '';
+ const halfData: OpenAIChat[] = [];
- let halfDataTokens = 0
- while(halfDataTokens < chunkSize){
- const chat = chats[idx]
- if(!chat){
- break
- }
- halfDataTokens += await tokenizer.tokenizeChat(chat)
- halfData.push(chat)
- idx++
- targetId = chat.memo
+ let halfDataTokens = 0;
+ while (halfDataTokens < chunkSize && (idx <= chats.length - 4)) { // Ensure latest two chats are not added to summarization.
+ const chat = chats[idx];
+ halfDataTokens += await tokenizer.tokenizeChat(chat);
+ halfData.push(chat);
+ idx++;
+ targetId = chat.memo;
+ console.log("current target chat: ", chat);
}
- const stringlizedChat = halfData.map(e => `${e.role}: ${e.content}`).join('\n')
+ // Avoid summarizing the last two chats
+ if (halfData.length < 3) break;
- const summaryData = await summary(stringlizedChat)
+ const stringlizedChat = halfData.map(e => `${e.role}: ${e.content}`).join('\n');
+ const summaryData = await summary(stringlizedChat);
- if(!summaryData.success){
- return {
- currentTokens: currentTokens,
- chats: chats,
- error: summaryData.data
+ if (!summaryData.success) {
+ summarizationFailures++;
+ if (summarizationFailures >= maxSummarizationFailures) {
+ return {
+ currentTokens: currentTokens,
+ chats: chats,
+ error: "Summarization failed multiple times. Aborting to prevent infinite loop."
+ };
}
+ continue;
}
- const summaryDataToken = await tokenizer.tokenizeChat({role:'system', content:summaryData.data})
- mainPrompt += `\n\n${summaryData.data}`
- currentTokens -= halfDataTokens
- allocatedTokens -= summaryDataToken
+ summarizationFailures = 0; // Reset failure counter on success
+
+ const summaryDataToken = await tokenizer.tokenizeChat({ role: 'system', content: summaryData.data });
+ mainPrompt += `\n\n${summaryData.data}`;
+ currentTokens -= halfDataTokens;
+ allocatedTokens -= summaryDataToken;
data.mainChunks.unshift({
- text: mainPrompt,
+ text: summaryData.data,
targetId: targetId
- })
+ });
- if(allocatedTokens < 1500){
- const summarizedMp = await summary(mainPrompt)
- const mpToken = await tokenizer.tokenizeChat({role:'system', content:mainPrompt})
- const summaryToken = await tokenizer.tokenizeChat({role:'system', content:summarizedMp.data})
+ // Split the summary into chunks based on double line breaks
+ const splitted = summaryData.data.split('\n\n').map(e => e.trim()).filter(e => e.length > 0);
- allocatedTokens -= summaryToken
- allocatedTokens += mpToken
+ // Update chunks with the new summary
+ data.chunks.push(...splitted.map(e => ({
+ text: e,
+ targetId: targetId
+ })));
- const splited = mainPrompt.split('\n\n').map(e => e.trim()).filter(e => e.length > 0)
-
- data.chunks.push(...splited.map(e => ({
- text: e,
- targetId: targetId
- })))
-
- data.mainChunks[0].text = mainPrompt
- }
+ // Remove summarized chats
+ chats.splice(0, idx);
}
-
- const processer = new HypaProcesser("nomic")
- await processer.addText(data.chunks.filter(v => {
- return v.text.trim().length > 0
- }).map((v) => {
- return "search_document: " + v.text.trim()
- }))
+ // Construct the mainPrompt from mainChunks until half of the allocatedTokens are used
+ mainPrompt = "";
+ let mainPromptTokens = 0;
+ for (const chunk of data.mainChunks) {
+ const chunkTokens = await tokenizer.tokenizeChat({ role: 'system', content: chunk.text });
+ if (mainPromptTokens + chunkTokens > allocatedTokens / 2) break;
+ mainPrompt += `\n\n${chunk.text}`;
+ mainPromptTokens += chunkTokens;
+ lastMainChunkTargetId = chunk.targetId;
+ }
- let scoredResults:{[key:string]:number} = {}
- for(let i=0;i<3;i++){
- const pop = chats[chats.length - i - 1]
- if(!pop){
- break
+ // Fetch additional memory from chunks
+ const processor = new HypaProcesser(db.hypaModel);
+ processor.oaikey = db.supaMemoryKey;
+
+ // Find the smallest index of chunks with the same targetId as lastMainChunkTargetId
+ const lastMainChunkIndex = data.chunks.reduce((minIndex, chunk, index) => {
+ if (chunk.targetId === lastMainChunkTargetId) {
+ return Math.min(minIndex, index);
}
- 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
- }
+ return minIndex;
+ }, data.chunks.length);
+
+ // Filter chunks to only include those older than the last mainChunk's targetId
+ const olderChunks = lastMainChunkIndex !== data.chunks.length
+ ? data.chunks.slice(0, lastMainChunkIndex)
+ : data.chunks;
+
+ console.log("Older Chunks:", olderChunks);
+
+ // Add older chunks to processor for similarity search
+ await processor.addText(olderChunks.filter(v => v.text.trim().length > 0).map(v => "search_document: " + v.text.trim()));
+
+ 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 processor.similaritySearchScored(`search_query: ${pop.content}`);
+ for (const result of searched) {
+ const score = result[1] / (i + 1);
+ scoredResults[result[0]] = (scoredResults[result[0]] || 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 scoredArray = Object.entries(scoredResults).sort((a, b) => b[1] - a[1]);
+ let chunkResultPrompts = "";
+ let chunkResultTokens = 0;
+ while (allocatedTokens - mainPromptTokens - chunkResultTokens > 0 && scoredArray.length > 0) {
+ const [text] = scoredArray.shift();
+ const tokenized = await tokenizer.tokenizeChat({ role: 'system', content: text.substring(14) });
+ if (tokenized > allocatedTokens - mainPromptTokens - chunkResultTokens) break;
+ chunkResultPrompts += text.substring(14) + '\n\n';
+ chunkResultTokens += tokenized;
}
-
- const fullResult = `
${mainPrompt}\n
${chunkResultPrompts}`
+ const fullResult = `
${mainPrompt}\n
${chunkResultPrompts}`;
chats.unshift({
role: "system",
content: fullResult,
memo: "supaMemory"
- })
+ });
+
+ // Add the remaining chats after the last mainChunk's targetId
+ const lastTargetId = data.mainChunks.length > 0 ? data.mainChunks[0].targetId : null;
+ if (lastTargetId) {
+ const lastIndex = getValidChatIndex(lastTargetId);
+ if (lastIndex !== -1) {
+ const remainingChats = chats.slice(lastIndex + 1);
+ chats = [chats[0], ...remainingChats];
+ }
+ }
+
+ // Add last two chats if they exist and are not duplicates
+ if (lastTwoChats.length === 2) {
+ const [lastChat1, lastChat2] = lastTwoChats;
+ if (!chats.some(chat => chat.memo === lastChat1.memo)) {
+ chats.push(lastChat1);
+ }
+ if (!chats.some(chat => chat.memo === lastChat2.memo)) {
+ chats.push(lastChat2);
+ }
+ }
+
+ console.log("model being used: ", db.hypaModel, db.supaModelType, "\nCurrent session tokens: ", currentTokens, "\nAll chats, including memory system prompt: ", chats, "\nMemory data, with all the chunks: ", data);
return {
currentTokens: currentTokens,
chats: chats,
memory: data
- }
-}
\ No newline at end of file
+ };
+}
diff --git a/src/ts/process/memory/supaMemory.ts b/src/ts/process/memory/supaMemory.ts
index b2614c12..19f1f209 100644
--- a/src/ts/process/memory/supaMemory.ts
+++ b/src/ts/process/memory/supaMemory.ts
@@ -183,7 +183,7 @@ export async function supaMemory(
async function summarize(stringlizedChat:string){
- if(db.supaMemoryType === 'distilbart'){
+ if(db.supaModelType === 'distilbart'){
try {
const sum = await runSummarizer(stringlizedChat)
return sum
@@ -204,7 +204,7 @@ export async function supaMemory(
let result = ''
- if(db.supaMemoryType !== 'subModel'){
+ if(db.supaModelType !== 'subModel'){
const promptbody = stringlizedChat + '\n\n' + supaPrompt + "\n\nOutput:"
const da = await globalFetch("https://api.openai.com/v1/completions",{
@@ -214,8 +214,8 @@ export async function supaMemory(
},
method: "POST",
body: {
- "model": db.supaMemoryType === 'curie' ? "text-curie-001"
- : db.supaMemoryType === 'instruct35' ? 'gpt-3.5-turbo-instruct'
+ "model": db.supaModelType === 'curie' ? "text-curie-001"
+ : db.supaModelType === 'instruct35' ? 'gpt-3.5-turbo-instruct'
: "text-davinci-003",
"prompt": promptbody,
"max_tokens": 600,
diff --git a/src/ts/storage/database.ts b/src/ts/storage/database.ts
index 959f9a15..5c6b19fb 100644
--- a/src/ts/storage/database.ts
+++ b/src/ts/storage/database.ts
@@ -230,8 +230,8 @@ export function setDatabase(data:Database){
if(checkNullish(data.supaMemoryKey)){
data.supaMemoryKey = ""
}
- if(checkNullish(data.supaMemoryType)){
- data.supaMemoryType = "none"
+ if(checkNullish(data.supaModelType)){
+ data.supaModelType = "none"
}
if(checkNullish(data.askRemoval)){
data.askRemoval = true
@@ -527,7 +527,7 @@ export interface Database{
useStreaming:boolean
palmAPI:string,
supaMemoryKey:string
- supaMemoryType:string
+ supaModelType:string
textScreenColor?:string
textBorder?:boolean
textScreenRounded?:boolean
@@ -569,6 +569,8 @@ export interface Database{
useAdditionalAssetsPreview:boolean,
usePlainFetch:boolean
hypaMemory:boolean
+ hypav2:boolean
+ memoryAlgorithmType:string // To enable new memory module/algorithms
proxyRequestModel:string
ooba:OobaSettings
ainconfig: AINsettings