Merge branch 'main' of https://github.com/kwaroran/RisuAI
This commit is contained in:
kwaroran
@@ -276,23 +276,31 @@
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<span class="text-textcolor mt-4">{language.type}</span>
|
||||
|
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<SelectInput value={
|
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$DataBase.supaMemoryType === 'hypaV2' ? 'hypaV2' :
|
||||
$DataBase.supaMemoryType !== 'none' ? 'supaMemory' :
|
||||
$DataBase.hypav2 ? 'hypaV2' :
|
||||
$DataBase.supaModelType !== 'none' ? 'supaMemory' :
|
||||
$DataBase.hanuraiEnable ? 'hanuraiMemory' : 'none'
|
||||
} on:change={(v) => {
|
||||
//@ts-ignore
|
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const value = v.target.value
|
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if (value === 'supaMemory'){
|
||||
$DataBase.supaMemoryType = 'distilbart'
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$DataBase.supaModelType = 'distilbart'
|
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$DataBase.memoryAlgorithmType = 'supaMemory'
|
||||
$DataBase.hypav2 = false
|
||||
$DataBase.hanuraiEnable = false
|
||||
} else if (value === 'hanuraiMemory'){
|
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$DataBase.supaMemoryType = 'none'
|
||||
$DataBase.supaModelType = 'none'
|
||||
$DataBase.memoryAlgorithmType = 'hanuraiMemory'
|
||||
$DataBase.hypav2 = false
|
||||
$DataBase.hanuraiEnable = true
|
||||
} else if (value === 'hypaV2') {
|
||||
$DataBase.supaMemoryType = 'hypaV2'
|
||||
$DataBase.supaModelType = 'distilbart'
|
||||
$DataBase.memoryAlgorithmType = 'hypaMemoryV2'
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||||
$DataBase.hypav2= true
|
||||
$DataBase.hanuraiEnable = false
|
||||
} else {
|
||||
$DataBase.supaMemoryType = 'none'
|
||||
$DataBase.supaModelType = 'none'
|
||||
$DataBase.memoryAlgorithmType = 'none'
|
||||
$DataBase.hypav2 = false
|
||||
$DataBase.hanuraiEnable = false
|
||||
}
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||||
}}>
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@@ -309,27 +317,45 @@
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<div class="flex">
|
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<Check bind:check={$DataBase.hanuraiSplit} name="Text Spliting"/>
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</div>
|
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{:else if $DataBase.supaMemoryType === 'hypaV2'}
|
||||
{:else if $DataBase.hypav2}
|
||||
<span class="mb-2 text-textcolor2 text-sm text-wrap break-words max-w-full">{language.hypaV2Desc}</span>
|
||||
<span class="text-textcolor mt-4">{language.SuperMemory} {language.model}</span>
|
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<SelectInput className="mt-2 mb-2" bind:value={$DataBase.supaModelType}>
|
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<OptionInput value="distilbart">distilbart-cnn-6-6 (Free/Local)</OptionInput>
|
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<OptionInput value="instruct35">OpenAI 3.5 Turbo Instruct</OptionInput>
|
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<OptionInput value="subModel">{language.submodel}</OptionInput>
|
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</SelectInput>
|
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{#if $DataBase.supaModelType === 'davinci' || $DataBase.supaModelType === 'curie' || $DataBase.supaModelType === 'instruct35'}
|
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<span class="text-textcolor">{language.SuperMemory} OpenAI Key</span>
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<TextInput size="sm" marginBottom bind:value={$DataBase.supaMemoryKey}/>
|
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{/if}
|
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<span class="text-textcolor">{language.SuperMemory} Prompt</span>
|
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<TextInput size="sm" marginBottom bind:value={$DataBase.supaMemoryPrompt} placeholder="Leave it blank to use default"/>
|
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<span class="text-textcolor">{language.HypaMemory} Model</span>
|
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<SelectInput className="mt-2 mb-2" bind:value={$DataBase.hypaModel}>
|
||||
<OptionInput value="MiniLM">MiniLM-L6-v2 (Free / Local)</OptionInput>
|
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<OptionInput value="nomic">Nomic (Free / Local)</OptionInput>
|
||||
<OptionInput value="ada">OpenAI Ada (Davinci / Curie Only)</OptionInput>
|
||||
</SelectInput>
|
||||
<span class="text-textcolor">{language.hypaChunkSize}</span>
|
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<NumberInput size="sm" marginBottom bind:value={$DataBase.hypaChunkSize} min={100} />
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<span class="text-textcolor">{language.hypaAllocatedTokens}</span>
|
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<NumberInput size="sm" marginBottom bind:value={$DataBase.hypaAllocatedTokens} min={100} />
|
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{:else if $DataBase.supaMemoryType !== 'none'}
|
||||
{:else if ($DataBase.supaModelType !== 'none' && $DataBase.hypav2 === false)}
|
||||
<span class="mb-2 text-textcolor2 text-sm text-wrap break-words max-w-full">{language.supaDesc}</span>
|
||||
<span class="text-textcolor mt-4">{language.SuperMemory} {language.model}</span>
|
||||
<SelectInput className="mt-2 mb-2" bind:value={$DataBase.supaMemoryType}>
|
||||
<SelectInput className="mt-2 mb-2" bind:value={$DataBase.supaModelType}>
|
||||
<OptionInput value="distilbart" >distilbart-cnn-6-6 (Free/Local)</OptionInput>
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<OptionInput value="instruct35" >OpenAI 3.5 Turbo Instruct</OptionInput>
|
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<OptionInput value="subModel" >{language.submodel}</OptionInput>
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</SelectInput>
|
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<span class="text-textcolor">{language.maxSupaChunkSize}</span>
|
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<NumberInput size="sm" marginBottom bind:value={$DataBase.maxSupaChunkSize} min={100} />
|
||||
{#if $DataBase.supaMemoryType === 'davinci' || $DataBase.supaMemoryType === 'curie' || $DataBase.supaMemoryType === 'instruct35'}
|
||||
{#if $DataBase.supaModelType === 'davinci' || $DataBase.supaModelType === 'curie' || $DataBase.supaModelType === 'instruct35'}
|
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<span class="text-textcolor">{language.SuperMemory} OpenAI Key</span>
|
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<TextInput size="sm" marginBottom bind:value={$DataBase.supaMemoryKey}/>
|
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{/if}
|
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{#if $DataBase.supaMemoryType !== 'none'}
|
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{#if $DataBase.supaModelType !== 'none'}
|
||||
<span class="text-textcolor">{language.SuperMemory} Prompt</span>
|
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<TextInput size="sm" marginBottom bind:value={$DataBase.supaMemoryPrompt} placeholder="Leave it blank to use default"/>
|
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{/if}
|
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|
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@@ -257,7 +257,7 @@
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{/each}
|
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{#if $DataBase.supaMemoryType !== 'none' || $DataBase.hanuraiEnable}
|
||||
{#if $DataBase.supaModelType !== 'none' || $DataBase.hanuraiEnable}
|
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{#if $DataBase.hanuraiEnable}
|
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<div class="flex mt-2 items-center">
|
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<Check bind:check={currentChar.data.supaMemory} name={ language.hanuraiMemory}/>
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@@ -904,7 +904,7 @@
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<span> <Help key="utilityBot" name={language.utilityBot}/></span>
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</div>
|
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|
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{#if $DataBase.supaMemoryType === 'hypaV2'}
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{#if $DataBase.supaModelType !== 'none' && $DataBase.hypav2}
|
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<Button
|
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on:click={() => {
|
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currentChar.data.chats[currentChar.data.chatPage].hypaV2Data ??= {
|
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|
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@@ -165,7 +165,7 @@
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</div>
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{/each}
|
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|
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{#if $DataBase.supaMemoryType !== 'none' || $DataBase.hanuraiEnable}
|
||||
{#if $DataBase.supaModelType !== 'none' || $DataBase.hanuraiEnable}
|
||||
{#if $DataBase.hanuraiEnable}
|
||||
<div class="flex mt-2 items-center">
|
||||
<CheckInput bind:check={chara.supaMemory} name={ language.hanuraiMemory}/>
|
||||
|
||||
@@ -714,7 +714,7 @@ export async function sendChat(chatProcessIndex = -1,arg:{chatAdditonalTokens?:n
|
||||
currentTokens += await tokenizer.tokenizeChat(chat)
|
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}
|
||||
|
||||
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
|
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chats = hn.chats
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currentTokens = hn.tokens
|
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}
|
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else if(db.supaMemoryType === 'hypaV2'){
|
||||
else if(db.hypav2){ //HypaV2 support needs to be changed like this.
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||||
const sp = await hypaMemoryV2(chats, currentTokens, maxContextTokens, currentChat, nowChatroom, tokenizer)
|
||||
console.log("All chats: ", chats)
|
||||
if(sp.error){
|
||||
console.log(sp)
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||||
alertError(sp.error)
|
||||
return false
|
||||
}
|
||||
|
||||
@@ -4,209 +4,306 @@ import type { ChatTokenizer } from "src/ts/tokenizer";
|
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import { get } from "svelte/store";
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import { requestChatData } from "../request";
|
||||
import { HypaProcesser } from "./hypamemory";
|
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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: ${pop.content}`);
|
||||
for (const result of searched) {
|
||||
const score = result[1] / (i + 1);
|
||||
scoredResults[result[0]] = (scoredResults[result[0]] || 0) + score;
|
||||
}
|
||||
}
|
||||
|
||||
const scoredArray = Object.entries(scoredResults).sort((a,b) => b[1] - a[1])
|
||||
|
||||
let chunkResultPrompts = ""
|
||||
while(allocatedTokens > 0){
|
||||
const target = scoredArray.shift()
|
||||
if(!target){
|
||||
break
|
||||
}
|
||||
const tokenized = await tokenizer.tokenizeChat({
|
||||
role: 'system',
|
||||
content: target[0].substring(14)
|
||||
})
|
||||
if(tokenized > allocatedTokens){
|
||||
break
|
||||
}
|
||||
chunkResultPrompts += target[0].substring(14) + '\n\n'
|
||||
allocatedTokens -= tokenized
|
||||
const scoredArray = Object.entries(scoredResults).sort((a, b) => b[1] - a[1]);
|
||||
let chunkResultPrompts = "";
|
||||
let chunkResultTokens = 0;
|
||||
while (allocatedTokens - mainPromptTokens - chunkResultTokens > 0 && scoredArray.length > 0) {
|
||||
const [text] = scoredArray.shift();
|
||||
const tokenized = await tokenizer.tokenizeChat({ role: 'system', content: text.substring(14) });
|
||||
if (tokenized > allocatedTokens - mainPromptTokens - chunkResultTokens) break;
|
||||
chunkResultPrompts += text.substring(14) + '\n\n';
|
||||
chunkResultTokens += tokenized;
|
||||
}
|
||||
|
||||
|
||||
const fullResult = `<Past Events Summary>${mainPrompt}</Past Events Summary>\n<Past Events Details>${chunkResultPrompts}</Past Events Details>`
|
||||
const fullResult = `<Past Events Summary>${mainPrompt}</Past Events Summary>\n<Past Events Details>${chunkResultPrompts}</Past Events Details>`;
|
||||
|
||||
chats.unshift({
|
||||
role: "system",
|
||||
content: fullResult,
|
||||
memo: "supaMemory"
|
||||
})
|
||||
});
|
||||
|
||||
// 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
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user