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Merge remote-tracking branch 'upstream/main'

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This commit is contained in:
LightningHyperBlaze45654
2024-12-08 20:08:25 -08:00
parent 0c51e898f9 71ef6099a8
commit 69f44c03c6
30 changed files with 887 additions and 298 deletions
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2
src/ts/process/memory/hanuraiMemory.ts
View File

@@ -13,7 +13,7 @@ export async function hanuraiMemory(chats:OpenAIChat[],arg:{
}){
const db = getDatabase()
const tokenizer = arg.tokenizer
const processer = new HypaProcesser('MiniLM')
const processer = new HypaProcesser()
let addTexts:string[] = []
const queryStartIndex=chats.length-maxRecentChatQuery
console.log(chats.length,maxRecentChatQuery,queryStartIndex)

60
src/ts/process/memory/hypamemory.ts
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@@ -1,22 +1,49 @@
import localforage from "localforage";
import {globalFetch} from "src/ts/globalApi.svelte";
import {runEmbedding} from "../transformers";
import {appendLastPath} from "src/ts/util";
import { globalFetch } from "src/ts/globalApi.svelte";
import { runEmbedding } from "../transformers";
import { alertError } from "src/ts/alert";
import { appendLastPath } from "src/ts/util";
import { getDatabase } from "src/ts/storage/database.svelte";
export type HypaModel = 'ada'|'MiniLM'|'nomic'|'custom'|'nomicGPU'|'bgeSmallEn'|'bgeSmallEnGPU'|'bgem3'|'bgem3GPU'|'openai3small'|'openai3large'
const localModels = {
models: {
'MiniLM':'Xenova/all-MiniLM-L6-v2',
'nomic':'nomic-ai/nomic-embed-text-v1.5',
'nomicGPU':'nomic-ai/nomic-embed-text-v1.5',
'bgeSmallEn': 'BAAI/bge-small-en-v1.5',
'bgeSmallEnGPU': 'BAAI/bge-small-en-v1.5',
'bgem3': 'BAAI/bge-m3',
'bgem3GPU': 'BAAI/bge-m3',
},
gpuModels:[
'nomicGPU',
'bgeSmallEnGPU',
'bgem3GPU'
]
}
export class HypaProcesser{
oaikey:string
vectors:memoryVector[]
forage:LocalForage
model:'ada'|'MiniLM'|'nomic'|'custom'
model:HypaModel
customEmbeddingUrl:string
constructor(model:'ada'|'MiniLM'|'nomic'|'custom',customEmbeddingUrl?:string){
constructor(model:HypaModel|'auto' = 'auto',customEmbeddingUrl?:string){
this.forage = localforage.createInstance({
name: "hypaVector"
})
this.vectors = []
this.model = model
if(model === 'auto'){
const db = getDatabase()
this.model = db.hypaModel || 'MiniLM'
}
else{
this.model = model
}
this.customEmbeddingUrl = customEmbeddingUrl
}
@@ -38,9 +65,9 @@ export class HypaProcesser{
async getEmbeds(input:string[]|string):Promise<VectorArray[]> {
if(this.model === 'MiniLM' || this.model === 'nomic'){
if(Object.keys(localModels.models).includes(this.model)){
const inputs:string[] = Array.isArray(input) ? input : [input]
let results:Float32Array[] = await runEmbedding(inputs, this.model === 'nomic' ? 'nomic-ai/nomic-embed-text-v1.5' : 'Xenova/all-MiniLM-L6-v2')
let results:Float32Array[] = await runEmbedding(inputs, localModels.models[this.model], localModels.gpuModels.includes(this.model) ? 'webgpu' : 'wasm')
return results
}
let gf = null;
@@ -57,14 +84,21 @@ export class HypaProcesser{
},
})
}
if(this.model === 'ada'){
if(this.model === 'ada' || this.model === 'openai3small' || this.model === 'openai3large'){
const db = getDatabase()
const models = {
'ada':'text-embedding-ada-002',
'openai3small':'text-embedding-3-small',
'openai3large':'text-embedding-3-large'
}
gf = await globalFetch("https://api.openai.com/v1/embeddings", {
headers: {
"Authorization": "Bearer " + this.oaikey
"Authorization": "Bearer " + db.supaMemoryKey || this.oaikey
},
body: {
"input": input,
"model": "text-embedding-ada-002"
"input": input,
"model": models[this.model]
}
})
}
@@ -138,7 +172,7 @@ export class HypaProcesser{
}
async similaritySearchScored(query: string) {
return await this.similaritySearchVectorWithScore((await this.getEmbeds(query))[0],)
return await this.similaritySearchVectorWithScore((await this.getEmbeds(query))[0],);
}
private async similaritySearchVectorWithScore(

42
src/ts/process/memory/hypav2.ts
View File

@@ -30,6 +30,7 @@ async function summary(
stringlizedChat: string
): Promise<{ success: boolean; data: string }> {
const db = getDatabase();
console.log("Summarizing");
if (db.supaModelType === "distilbart") {
try {
@@ -101,35 +102,27 @@ async function summary(
supaPrompt.replaceAll("{{slot}}", stringlizedChat)
);
const promptbody: OpenAIChat[] = parsedPrompt ?? [
const promptbody: OpenAIChat[] = (parsedPrompt ?? [
{
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,
},
"memory"
);
if (
da.type === "fail" ||
da.type === "streaming" ||
da.type === "multiline"
) {
content: supaPrompt
}
]).map(message => ({
...message,
memo: "supaPrompt"
}));
console.log("Using submodel: ", db.subModel, "for supaMemory model");
const da = await requestChatData({
formated: promptbody,
bias: {},
useStreaming: false,
noMultiGen: true
}, 'memory');
if (da.type === 'fail' || da.type === 'streaming' || da.type === 'multiline') {
return {
success: false,
data: "SupaMemory: HTTP: " + da.result,
@@ -179,7 +172,7 @@ export async function regenerateSummary(
mainChunkIndex: number
) : Promise<void> {
const targetMainChunk = data.mainChunks[mainChunkIndex];
}
export async function hypaMemoryV2(
chats: OpenAIChat[],
@@ -209,6 +202,7 @@ export async function hypaMemoryV2(
currentTokens += allocatedTokens + chats.length * 4; // ChatML token counting from official openai documentation
let mainPrompt = "";
const lastTwoChats = chats.slice(-2);
// Error handling for infinite summarization attempts
let summarizationFailures = 0;
const maxSummarizationFailures = 3;