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17 Commits
0.5.3 ... 0.7.1

Author SHA1 Message Date
7ef7c3f676 Cap speaker ID transcript length to 2000 tokens
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2026-02-14 09:48:12 -05:00
4143d00de7 Working speaker detection with advanced LLM identifying. Improved LLM json function
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2026-02-14 09:39:17 -05:00
0360f2493d Added hugging face token
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2026-02-12 22:15:57 -05:00
0172887877 audio worker fix
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2026-02-12 20:24:12 -05:00
8f89f5e3cf embedding worker fix
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2026-02-12 20:18:56 -05:00
5bd41f8c6a worker fix?
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2026-02-12 20:17:31 -05:00
e4399e1b7b Updataes?
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2026-02-12 20:14:00 -05:00
ad1ee48763 Use one-off workers to process requests without blocking
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2026-02-12 19:45:17 -05:00
3ed206923f Fix ASR
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2026-02-12 18:32:19 -05:00
22d5427e86 Fix ASR
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2026-02-12 17:49:33 -05:00
43b53164c0 Bump 0.6.3
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2026-02-12 17:24:15 -05:00
575fbac099 Fixed ASR
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2026-02-12 13:31:30 -05:00
46ae0f7913 expose diarization support checking function
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2026-02-12 11:55:29 -05:00
54730a2b9a Speaker diarization
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2026-02-12 11:26:11 -05:00
27506d20af Fix anthropic message history
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2026-02-11 22:45:30 -05:00
8c64129200 Removed log statement
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2026-02-11 21:58:39 -05:00
013aa942c0 Added save directory for embedder
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2026-02-11 21:45:54 -05:00
13 changed files with 432 additions and 269 deletions

View File

@@ -75,6 +75,7 @@ A TypeScript library that provides a unified interface for working with multiple
#### Instructions
1. Install the package: `npm i @ztimson/ai-utils`
2. For speaker diarization: `pip install pyannote.audio`
</details>
@@ -90,8 +91,9 @@ A TypeScript library that provides a unified interface for working with multiple
#### Instructions
1. Install the dependencies: `npm i`
2. Build library: `npm build`
3. Run unit tests: `npm test`
2. For speaker diarization: `pip install pyannote.audio`
3. Build library: `npm build`
4. Run unit tests: `npm test`
</details>

337
package-lock.json generated
View File

@@ -1,12 +1,12 @@
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"requires": true,
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"version": "0.6.4",
"license": "MIT",
"dependencies": {
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@@ -16,7 +16,8 @@
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"openai": "^6.6.0",
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"tesseract.js": "^6.0.1",
"wavefile": "^11.0.0"
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@@ -157,9 +158,9 @@
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@@ -174,9 +175,9 @@
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@@ -191,9 +192,9 @@
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@@ -208,9 +209,9 @@
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@@ -225,9 +226,9 @@
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@@ -242,9 +243,9 @@
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@@ -259,9 +260,9 @@
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@@ -276,9 +277,9 @@
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@@ -293,9 +294,9 @@
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@@ -310,9 +311,9 @@
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@@ -327,9 +328,9 @@
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@@ -344,9 +345,9 @@
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@@ -361,9 +362,9 @@
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@@ -378,9 +379,9 @@
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@@ -395,9 +396,9 @@
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@@ -3994,9 +3995,9 @@
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@@ -4403,9 +4404,9 @@
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@@ -4667,6 +4668,18 @@
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View File

@@ -1,6 +1,6 @@
{
"name": "@ztimson/ai-utils",
"version": "0.5.3",
"version": "0.7.1",
"description": "AI Utility library",
"author": "Zak Timson",
"license": "MIT",
@@ -32,7 +32,8 @@
"@ztimson/utils": "^0.27.9",
"cheerio": "^1.2.0",
"openai": "^6.6.0",
"tesseract.js": "^6.0.1"
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"wavefile": "^11.0.0"
},
"devDependencies": {
"@types/node": "^24.8.1",

View File

@@ -8,26 +8,20 @@ export type AbortablePromise<T> = Promise<T> & {
};
export type AiOptions = {
/** Token to pull models from hugging face */
hfToken?: string;
/** Path to models */
path?: string;
/** Embedding model */
embedder?: string; // all-MiniLM-L6-v2, bge-small-en-v1.5, bge-large-en-v1.5
/** ASR model: whisper-tiny, whisper-base */
asr?: string;
/** Embedding model: all-MiniLM-L6-v2, bge-small-en-v1.5, bge-large-en-v1.5 */
embedder?: string;
/** Large language models, first is default */
llm?: Omit<LLMRequest, 'model'> & {
models: {[model: string]: AnthropicConfig | OllamaConfig | OpenAiConfig};
}
/** Tesseract OCR configuration */
tesseract?: {
/** Model: eng, eng_best, eng_fast */
model?: string;
}
/** Whisper ASR configuration */
whisper?: {
/** Whisper binary location */
binary: string;
/** Model: `ggml-base.en.bin` */
model: string;
}
/** OCR model: eng, eng_best, eng_fast */
ocr?: string;
}
export class Ai {

View File

@@ -13,25 +13,25 @@ export class Anthropic extends LLMProvider {
}
private toStandard(history: any[]): LLMMessage[] {
for(let i = 0; i < history.length; i++) {
const orgI = i;
if(typeof history[orgI].content != 'string') {
if(history[orgI].role == 'assistant') {
history[orgI].content.filter((c: any) => c.type =='tool_use').forEach((c: any) => {
history.splice(i + 1, 0, {role: 'tool', id: c.id, name: c.name, args: c.input, timestamp: Date.now()});
});
} else if(history[orgI].role == 'user') {
history[orgI].content.filter((c: any) => c.type =='tool_result').forEach((c: any) => {
const h = history.find((h: any) => h.id == c.tool_use_id);
h[c.is_error ? 'error' : 'content'] = c.content;
});
}
history[orgI].content = history[orgI].content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
if(!history[orgI].content) history.splice(orgI, 1);
const timestamp = Date.now();
const messages: LLMMessage[] = [];
for(let h of history) {
if(typeof h.content == 'string') {
messages.push(<any>{timestamp, ...h});
} else {
const textContent = h.content?.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
if(textContent) messages.push({timestamp, role: h.role, content: textContent});
h.content.forEach((c: any) => {
if(c.type == 'tool_use') {
messages.push({timestamp, role: 'tool', id: c.id, name: c.name, args: c.input, content: undefined});
} else if(c.type == 'tool_result') {
const m: any = messages.findLast(m => (<any>m).id == c.tool_use_id);
if(m) m[c.is_error ? 'error' : 'content'] = c.content;
}
});
}
if(!history[orgI].timestamp) history[orgI].timestamp = Date.now();
}
return history.filter(h => !!h.content);
return messages;
}
private fromStandard(history: LLMMessage[]): any[] {
@@ -50,8 +50,8 @@ export class Anthropic extends LLMProvider {
ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
const controller = new AbortController();
return Object.assign(new Promise<any>(async (res, rej) => {
const history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
return Object.assign(new Promise<any>(async (res) => {
let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
const tools = options.tools || this.ai.options.llm?.tools || [];
const requestParams: any = {
model: options.model || this.model,
@@ -73,7 +73,6 @@ export class Anthropic extends LLMProvider {
};
let resp: any, isFirstMessage = true;
const assistantMessages: string[] = [];
do {
resp = await this.client.messages.create(requestParams).catch(err => {
err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
@@ -119,7 +118,6 @@ export class Anthropic extends LLMProvider {
if(options.stream) options.stream({tool: toolCall.name});
if(!tool) return {tool_use_id: toolCall.id, is_error: true, content: 'Tool not found'};
try {
console.log(typeof tool.fn);
const result = await tool.fn(toolCall.input, options?.stream, this.ai);
return {type: 'tool_result', tool_use_id: toolCall.id, content: JSONSanitize(result)};
} catch (err: any) {
@@ -131,7 +129,7 @@ export class Anthropic extends LLMProvider {
}
} while (!controller.signal.aborted && resp.content.some((c: any) => c.type === 'tool_use'));
history.push({role: 'assistant', content: resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n')});
this.toStandard(history);
history = this.toStandard(history);
if(options.stream) options.stream({done: true});
if(options.history) options.history.splice(0, options.history.length, ...history);

134
src/asr.ts Normal file
View File

@@ -0,0 +1,134 @@
import { pipeline } from '@xenova/transformers';
import { parentPort } from 'worker_threads';
import { spawn } from 'node:child_process';
import { execSync } from 'node:child_process';
import { mkdtempSync, rmSync, readFileSync } from 'node:fs';
import { join } from 'node:path';
import { tmpdir } from 'node:os';
import wavefile from 'wavefile';
let whisperPipeline: any;
export async function canDiarization(): Promise<boolean> {
return new Promise((resolve) => {
const proc = spawn('python', ['-c', 'import pyannote.audio']);
proc.on('close', (code: number) => resolve(code === 0));
proc.on('error', () => resolve(false));
});
}
async function runDiarization(audioPath: string, dir: string, token: string): Promise<any[]> {
const script = `
import sys
import json
import os
from pyannote.audio import Pipeline
os.environ['TORCH_HOME'] = r"${dir}"
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${token}")
output = pipeline(sys.argv[1])
segments = []
for turn, speaker in output.speaker_diarization:
segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
print(json.dumps(segments))
`;
return new Promise((resolve, reject) => {
let output = '';
const proc = spawn('python', ['-c', script, audioPath]);
proc.stdout.on('data', (data: Buffer) => output += data.toString());
proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
proc.on('close', (code: number) => {
if(code === 0) {
try {
resolve(JSON.parse(output));
} catch (err) {
reject(new Error('Failed to parse diarization output'));
}
} else {
reject(new Error(`Python process exited with code ${code}`));
}
});
proc.on('error', reject);
});
}
function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
const speakerMap = new Map();
let speakerCount = 0;
speakers.forEach((seg: any) => {
if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
});
const lines: string[] = [];
let currentSpeaker = -1;
let currentText = '';
chunks.forEach((chunk: any) => {
const time = chunk.timestamp[0];
const speaker = speakers.find((s: any) => time >= s.start && time <= s.end);
const speakerNum = speaker ? speakerMap.get(speaker.speaker) : 1;
if (speakerNum !== currentSpeaker) {
if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
currentSpeaker = speakerNum;
currentText = chunk.text;
} else {
currentText += chunk.text;
}
});
if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
return lines.join('\n');
}
function prepareAudioBuffer(file: string): [string, Float32Array] {
let wav: any, tmp;
try {
wav = new wavefile.WaveFile(readFileSync(file));
} catch(err) {
tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
wav = new wavefile.WaveFile(readFileSync(tmp));
} finally {
wav.toBitDepth('32f');
wav.toSampleRate(16000);
const samples = wav.getSamples();
if(Array.isArray(samples)) {
const left = samples[0];
const right = samples[1];
const buffer = new Float32Array(left.length);
for (let i = 0; i < left.length; i++) buffer[i] = (left[i] + right[i]) / 2;
return [tmp || file, buffer];
}
return [tmp || file, samples];
}
}
parentPort?.on('message', async ({ file, speaker, model, modelDir, token }) => {
try {
if(!whisperPipeline) whisperPipeline = await pipeline('automatic-speech-recognition', `Xenova/${model}`, {cache_dir: modelDir, quantized: true});
// Prepare audio file
const [f, buffer] = prepareAudioBuffer(file);
// Fetch transcript and speakers
const hasDiarization = speaker && await canDiarization();
const [transcript, speakers] = await Promise.all([
whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false}),
(!speaker || !token || !hasDiarization) ? Promise.resolve(): runDiarization(f, modelDir, token),
]);
if(file != f) rmSync(f, { recursive: true, force: true });
// Return any results / errors if no more processing required
const text = transcript.text?.trim() || null;
if(!speaker) return parentPort?.postMessage({ text });
if(!token) return parentPort?.postMessage({ text, error: 'HuggingFace token required' });
if(!hasDiarization) return parentPort?.postMessage({ text, error: 'Speaker diarization unavailable' });
// Combine transcript and speakers
const combined = combineSpeakerTranscript(transcript.chunks || [], speakers || []);
parentPort?.postMessage({ text: combined });
} catch (err: any) {
parentPort?.postMessage({ error: err.stack || err.message });
}
});

View File

@@ -1,50 +1,60 @@
import {spawn} from 'node:child_process';
import fs from 'node:fs/promises';
import Path from 'node:path';
import {fileURLToPath} from 'url';
import {Worker} from 'worker_threads';
import {AbortablePromise, Ai} from './ai.ts';
import {canDiarization} from './asr.ts';
import {dirname, join} from 'path';
export class Audio {
private downloads: {[key: string]: Promise<string>} = {};
private whisperModel!: string;
constructor(private ai: Ai) {}
constructor(private ai: Ai) {
if(ai.options.whisper?.binary) {
this.whisperModel = ai.options.whisper?.model.endsWith('.bin') ? ai.options.whisper?.model : ai.options.whisper?.model + '.bin';
this.downloadAsrModel();
}
}
asr(file: string, options: { model?: string; speaker?: boolean | 'id' } = {}): AbortablePromise<string | null> {
const { model = this.ai.options.asr || 'whisper-base', speaker = false } = options;
let aborted = false;
const abort = () => { aborted = true; };
asr(path: string, model: string = this.whisperModel): AbortablePromise<string | null> {
if(!this.ai.options.whisper?.binary) throw new Error('Whisper not configured');
let abort: any = () => {};
const p = new Promise<string | null>(async (resolve, reject) => {
const m = await this.downloadAsrModel(model);
let output = '';
const proc = spawn(<string>this.ai.options.whisper?.binary, ['-nt', '-np', '-m', m, '-f', path], {stdio: ['ignore', 'pipe', 'ignore']});
abort = () => proc.kill('SIGTERM');
proc.on('error', (err: Error) => reject(err));
proc.stdout.on('data', (data: Buffer) => output += data.toString());
proc.on('close', (code: number) => {
if(code === 0) resolve(output.trim() || null);
else reject(new Error(`Exit code ${code}`));
let p = new Promise<string | null>((resolve, reject) => {
const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'asr.js'));
const handleMessage = ({ text, warning, error }: any) => {
worker.terminate();
if(aborted) return;
if(error) reject(new Error(error));
else {
if(warning) console.warn(warning);
resolve(text);
}
};
const handleError = (err: Error) => {
worker.terminate();
if(!aborted) reject(err);
};
worker.on('message', handleMessage);
worker.on('error', handleError);
worker.on('exit', (code) => {
if(code !== 0 && !aborted) reject(new Error(`Worker exited with code ${code}`));
});
worker.postMessage({file, model, speaker, modelDir: this.ai.options.path, token: this.ai.options.hfToken});
});
return Object.assign(p, {abort});
// Name speakers using AI
if(options.speaker == 'id') {
if(!this.ai.language.defaultModel) throw new Error('Configure an LLM for advanced ASR speaker detection');
p = p.then(async transcript => {
if(!transcript) return transcript;
let chunks = this.ai.language.chunk(transcript, 500, 0);
if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name"}', {
system: 'Use this following transcript to identify speakers. Only identify speakers you are sure about',
temperature: 0.1,
});
Object.entries(names).forEach(([speaker, name]) => {
transcript = (<string>transcript).replaceAll(`[Speaker ${speaker}]`, `[${name}]`);
});
return transcript;
})
}
return Object.assign(p, { abort });
}
async downloadAsrModel(model: string = this.whisperModel): Promise<string> {
if(!this.ai.options.whisper?.binary) throw new Error('Whisper not configured');
if(!model.endsWith('.bin')) model += '.bin';
const p = Path.join(<string>this.ai.options.path, model);
if(await fs.stat(p).then(() => true).catch(() => false)) return p;
if(!!this.downloads[model]) return this.downloads[model];
this.downloads[model] = fetch(`https://huggingface.co/ggerganov/whisper.cpp/resolve/main/${model}`)
.then(resp => resp.arrayBuffer())
.then(arr => Buffer.from(arr)).then(async buffer => {
await fs.writeFile(p, buffer);
delete this.downloads[model];
return p;
});
return this.downloads[model];
}
canDiarization = canDiarization;
}

View File

@@ -3,9 +3,9 @@ import { parentPort } from 'worker_threads';
let embedder: any;
parentPort?.on('message', async ({ id, text, model }) => {
if(!embedder) embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true});
parentPort?.on('message', async ({text, model, modelDir }) => {
if(!embedder) embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
const output = await embedder(text, { pooling: 'mean', normalize: true });
const embedding = Array.from(output.data);
parentPort?.postMessage({ id, embedding });
parentPort?.postMessage({embedding});
});

View File

@@ -1,5 +1,6 @@
export * from './ai';
export * from './antrhopic';
export * from './asr';
export * from './audio';
export * from './embedder'
export * from './llm';

View File

@@ -75,22 +75,10 @@ export type LLMRequest = {
}
class LLM {
private embedWorker: Worker | null = null;
private embedQueue = new Map<number, { resolve: (value: number[]) => void; reject: (error: any) => void }>();
private embedId = 0;
private models: {[model: string]: LLMProvider} = {};
private defaultModel!: string;
defaultModel!: string;
models: {[model: string]: LLMProvider} = {};
constructor(public readonly ai: Ai) {
this.embedWorker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
this.embedWorker.on('message', ({ id, embedding }) => {
const pending = this.embedQueue.get(id);
if (pending) {
pending.resolve(embedding);
this.embedQueue.delete(id);
}
});
if(!ai.options.llm?.models) return;
Object.entries(ai.options.llm.models).forEach(([model, config]) => {
if(!this.defaultModel) this.defaultModel = model;
@@ -196,7 +184,12 @@ class LLM {
const system = history[0].role == 'system' ? history[0] : null,
recent = keep == 0 ? [] : history.slice(-keep),
process = (keep == 0 ? history : history.slice(0, -keep)).filter(h => h.role === 'assistant' || h.role === 'user');
const summary: any = await this.json(`Create the smallest summary possible, no more than 500 tokens. Create a list of NEW facts (split by subject [pro]noun and fact) about what you learned from this conversation that you didn't already know or get from a tool call or system prompt. Focus only on new information about people, topics, or facts. Avoid generating facts about the AI. Match this format: {summary: string, facts: [[subject, fact]]}\n\n${process.map(m => `${m.role}: ${m.content}`).join('\n\n')}`, {model: options?.model, temperature: options?.temperature || 0.3});
const summary: any = await this.json(process.map(m => `${m.role}: ${m.content}`).join('\n\n'), '{summary: string, facts: [[subject, fact]]}', {
system: 'Create the smallest summary possible, no more than 500 tokens. Create a list of NEW facts (split by subject [pro]noun and fact) about what you learned from this conversation that you didn\'t already know or get from a tool call or system prompt. Focus only on new information about people, topics, or facts. Avoid generating facts about the AI.',
model: options?.model,
temperature: options?.temperature || 0.3
});
const timestamp = new Date();
const memory = await Promise.all((summary?.facts || [])?.map(async ([owner, fact]: [string, string]) => {
const e = await Promise.all([this.embedding(owner), this.embedding(`${owner}: ${fact}`)]);
@@ -269,9 +262,21 @@ class LLM {
embedding(target: object | string, maxTokens = 500, overlapTokens = 50) {
const embed = (text: string): Promise<number[]> => {
return new Promise((resolve, reject) => {
const id = this.embedId++;
this.embedQueue.set(id, { resolve, reject });
this.embedWorker?.postMessage({ id, text, model: this.ai.options?.embedder || 'bge-small-en-v1.5' });
const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
const handleMessage = ({ embedding }: any) => {
worker.terminate();
resolve(embedding);
};
const handleError = (err: Error) => {
worker.terminate();
reject(err);
};
worker.on('message', handleMessage);
worker.on('error', handleError);
worker.on('exit', (code) => {
if(code !== 0) reject(new Error(`Worker exited with code ${code}`));
});
worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', modelDir: this.ai.options.path});
});
};
const chunks = this.chunk(target, maxTokens, overlapTokens);
@@ -312,12 +317,16 @@ class LLM {
/**
* Ask a question with JSON response
* @param {string} message Question
* @param {string} text Text to process
* @param {string} schema JSON schema the AI should match
* @param {LLMRequest} options Configuration options and chat history
* @returns {Promise<{} | {} | RegExpExecArray | null>}
*/
async json(message: string, options?: LLMRequest): Promise<any> {
let resp = await this.ask(message, {system: 'Respond using a JSON blob matching any provided examples', ...options});
async json(text: string, schema: string, options?: LLMRequest): Promise<any> {
let resp = await this.ask(text, {...options, system: (options?.system ? `${options.system}\n` : '') + `Only respond using a JSON code block matching this schema:
\`\`\`json
${schema}
\`\`\``});
if(!resp) return {};
const codeBlock = /```(?:.+)?\s*([\s\S]*?)```/.exec(resp);
const jsonStr = codeBlock ? codeBlock[1].trim() : resp;

View File

@@ -68,7 +68,7 @@ export class OpenAi extends LLMProvider {
const controller = new AbortController();
return Object.assign(new Promise<any>(async (res, rej) => {
if(options.system && options.history?.[0]?.role != 'system') options.history?.splice(0, 0, {role: 'system', content: options.system, timestamp: Date.now()});
const history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
const tools = options.tools || this.ai.options.llm?.tools || [];
const requestParams: any = {
model: options.model || this.model,
@@ -133,7 +133,7 @@ export class OpenAi extends LLMProvider {
}
} while (!controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
history.push({role: 'assistant', content: resp.choices[0].message.content || ''});
this.toStandard(history);
history = this.toStandard(history);
if(options.stream) options.stream({done: true});
if(options.history) options.history.splice(0, options.history.length, ...history);

View File

@@ -13,7 +13,7 @@ export class Vision {
ocr(path: string): AbortablePromise<string | null> {
let worker: any;
const p = new Promise<string | null>(async res => {
worker = await createWorker(this.ai.options.tesseract?.model || 'eng', 2, {cachePath: this.ai.options.path});
worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
const {data} = await worker.recognize(path);
await worker.terminate();
res(data.text.trim() || null);

View File

@@ -6,6 +6,7 @@ export default defineConfig({
build: {
lib: {
entry: {
asr: './src/asr.ts',
index: './src/index.ts',
embedder: './src/embedder.ts',
},