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Repository: a-r-d/vector-2-trend
Branch: main
Commit: 55d1ee01b4ce
Files: 17
Total size: 3.5 MB
Directory structure:
gitextract_bz2543u5/
├── .eslintrc.js
├── .gitignore
├── .npmignore
├── .nvmrc
├── .prettierrc
├── LICENSE
├── README.md
├── jest.config.js
├── package.json
├── scripts/
│ └── demo.js
├── src/
│ ├── index.ts
│ ├── sample-data.json
│ ├── types.ts
│ ├── utils.ts
│ ├── vector2Trend.ts
│ └── vector2trends.test.ts
└── tsconfig.json
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FILE CONTENTS
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FILE: .eslintrc.js
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module.exports = {
root: true,
parser: '@typescript-eslint/parser',
plugins: ['@typescript-eslint'],
extends: [
'eslint:recommended',
'plugin:@typescript-eslint/eslint-recommended',
'plugin:@typescript-eslint/recommended',
],
};
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FILE: .gitignore
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# Logs
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FILE: .npmignore
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src/
*.test.ts
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FILE: .nvmrc
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v16.15.0
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FILE: .prettierrc
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{
"semi": true,
"trailingComma": "all",
"singleQuote": true,
"printWidth": 80,
"tabWidth": 2
}
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FILE: LICENSE
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MIT License
Copyright (c) 2023 Aaron Decker
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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FILE: README.md
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# vector-2-trend
Take (text) vectors and get out trending topics using clustering algorithms.
```
npm i vector-2-trend
```
Good use cases:
- surfacing topics in user feedback
- categorizing survey responses
## Summary
You have some semantic vectors for text and would like to show trending topics, yes? Of course you do, it's a common use case. It's a few steps to do this however. Generally it involves clustering, and then summarizing the clusters somehow. This module helps you do that (I developed this code while working on an experiment called saymore.ai).
For clustering we use `kmeans` but will be adding more algorithms.
For classification, we use GPT-3.5-turbo using openai API (just use the API key).
## Interface
### The format of the Vectors:
```typescript
import { DataPoint } from 'vector-2-trend'
interface DataPoint {
id: number | string
text: string,
vector: number[]
}
const vectors: DataPoint[] = [{
id: 'abc-123',
text: 'some text',
vector: [0, 1, 2, 3 ... n]
}]
```
### Clustering the vectors:
```typescript
import Vector2Trend, {
ClusteringArgument,
ClusteringResult,
} from 'vector-2-trend';
type ClusteringArgument = {
records: DataPoint[];
n: number;
pcaDimensions: number;
// we will add more in the future
clusteringAlgorithm: 'kmeans';
};
const clusteringResult: ClusteringResult = Vector2Trend.cluster({
vectors: vectors,
// Dimensionality of the vectors - any number, but make sure they are uniform
// 1536 is the dimensionality of the text-embedding-ada-002 model by openAI
// which is cheap and performant (3k pages per dollar in 2023)
n: 1536,
// tune this based on your own data
// if you make this the same number as N PCA will essentially be skipped
// but with high dimensional data I would recommend a number that is reasonable
// for performance considerations.
pcaDimensions: 10,
// algorithm
clusteringAlgorithm: 'kmeans',
});
```
### Classifying the vectors:
```typescript
import { ClassifiedClusterResponse } from 'vector-2-trend';
const classificationResult: ClassifiedClusterResponse[] = Vector2Trend.classify(
{
openAiApiKey: string,
clusteringResult: ClusteringResult,
nTopics: 10, // optional, defaults to 10. How many topics to try to categorize in GPT.
temperature: 0.1, // optional, defaults to 0.1
elementsPerGroup: 10, // optional defaults to 10 in case you have very large clusters & hit token limit
},
);
```
### Using the results:
```typescript
// the type returned
export type ClassifiedClusterResponse = {
// this is the density score
score: number;
// these are the same as from the cluster ranking result
clusterId: number;
count: number;
records: DataPoint[];
// generated from GPT
name: string;
};
const classificationResults: ClassifiedClusterResponse[] = [] /// see above - call Vector2Trend.classify
console.log(classificationResults.map(x => ({ topic: x.name, score: x.score })))
```
Would look something like this:
### Debugging / Logging
If something is going wrong with classification you can toggle on debug logging
```typescript
Vector2Trend.enableLogging();
```
## Developing
I will take PRs, just please make sure to add tests where you change functionality.
Jest tests are configured.
```
npm run test
```
### Demo script
You can run the demo script locally, just change the openAI Api key.
```
npm run demo
```
The demo output should look like this:
## Your feedback
I am looking for your feedback to see what this could evolve into! Some things I have considered:
- adding in features to do initial vectorization
- graphical output features
- many more ways to tweak and use the clustering algorithm.
- more & better tests
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FILE: jest.config.js
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module.exports = {
roots: ['<rootDir>/src'],
transform: {
'^.+\\.tsx?$': 'ts-jest',
},
testRegex: '(/__tests__/.*|(\\.|/)(test|spec))\\.tsx?$',
moduleFileExtensions: ['ts', 'tsx', 'js', 'jsx', 'json', 'node'],
};
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FILE: package.json
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{
"name": "vector-2-trend",
"version": "1.0.0",
"description": "take semantic text vectors, run clustering, classification and output trends",
"main": "dist/index.js",
"types": "dist/index.d.ts",
"scripts": {
"test": "jest",
"build": "tsc",
"prepublishOnly": "npm run build",
"demo": "node scripts/demo.js"
},
"repository": {
"type": "git",
"url": "git+https://github.com/a-r-d/vector-2-trend.git"
},
"keywords": [
"semantic",
"vectors",
"clustering",
"machine",
"learning",
"trends"
],
"author": "Aaron Decker",
"license": "MIT",
"bugs": {
"url": "https://github.com/a-r-d/vector-2-trend/issues"
},
"homepage": "https://github.com/a-r-d/vector-2-trend#readme",
"devDependencies": {
"@types/jest": "^29.5.3",
"@types/lodash": "^4.14.195",
"@types/node": "^16.15.0",
"@typescript-eslint/eslint-plugin": "^6.1.0",
"@typescript-eslint/parser": "^6.1.0",
"eslint": "^8.45.0",
"jest": "^29.6.1",
"prettier": "^3.0.0",
"ts-jest": "^29.1.1",
"typescript": "^4.9.3"
},
"dependencies": {
"lodash": "^4.17.21",
"ml-kmeans": "^6.0.0",
"ml-pca": "^4.1.1",
"openai": "^3.3.0",
"tslog": "^4.8.2"
}
}
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FILE: scripts/demo.js
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const Vector2Trends = require('../dist/index');
const fs = require('fs');
const testData = fs.readFileSync('./src/sample-data.json', 'utf8');
const testDataPoints = JSON.parse(testData);
const clusters = Vector2Trends.default.cluster({
records: testDataPoints,
n: 1536,
pcaDimensions: 20,
clusteringAlgorithm: 'kmeans',
});
console.log(clusters);
const classify = async () => {
const calssifications = await Vector2Trends.default.classify({
clusteringResult: clusters,
// TODO: change this to your own API key
openAiApiKey: 'sk-xxxxxxxxxxxxxxxxxxx',
});
console.log(calssifications);
// topics:
console.log(calssifications.map(x => ({ topic: x.name, score: x.score })))
return calssifications;
};
classify();
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FILE: src/index.ts
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import { Vector2Trend } from "./vector2Trend";
export default Vector2Trend;
export * from "./types";
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FILE: src/sample-data.json
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[{"id":"c71a0b10-4330-4533-b78d-9dba3c194996","text":"I'm Lancelot of Inner Circle ⭕️ \"The bad boys of Reggae\" are you interested in doing an endorsement?","vector":[-0.017546259,-0.016090808,-0.019473383,-0.02586119,-0.0149048865,0.03250505,-0.0077421856,-0.025497328,-0.017681023,0.010484631,0.013227076,0.014958792,0.005464676,-0.014379308,0.019122997,0.010686777,0.032073807,-0.016333384,0.016481625,0.025160419,0.00038133972,-0.007964546,-0.0025335613,-0.016737675,0.01668377,-0.010969781,0.026750633,-0.00014760857,0.015956044,-0.0053501264,0.015484371,0.021157932,0.010329653,-0.0032326488,-0.018489607,-0.01862437,0.013570724,-0.011663815,0.044175606,0.015619135,0.007607422,0.0052086245,0.009878193,-0.038434662,0.0028906856,0.018799564,0.023031149,-0.022936815,-0.0065899543,0.038542476,0.008988752,-0.00244428,0.00373296,-0.018570464,-0.004073239,0.017088061,0.021629605,-0.0053467574,0.0006531837,0.0005996993,0.012364588,-0.012095059,-0.01645467,-0.017681023,-0.019365571,0.008321671,-0.007230083,-0.0018984864,-0.013004716,-0.010828279,0.00078584196,-0.0006704504,-0.0030271339,0.002299409,0.002452703,-0.023233294,-0.047302127,-0.005110921,-0.008725963,-0.005110921,0.015807806,-0.0071559628,-0.032639816,0.014473642,0.018166173,0.026642822,-0.0016710723,0.019042138,-0.005724096,-0.0001950918,-0.0013274243,0.029189859,0.0002910058,0.004076608,-0.031588655,0.0066068,-0.018004457,0.013355101,0.0017822525,-0.009413258,0.010457678,-0.02202042,-0.01653553,-0.009130253,-0.0069066496,-0.008530554,-0.019325143,-0.004076608,0.0373296,0.016077332,-0.018206602,0.028300418,0.008389052,-0.022613382,-0.0057274657,-0.0019810293,-0.004908775,-0.02003939,-0.02056497,-0.0103027,0.01813922,-0.011414502,0.016670294,-0.030187113,0.010909137,0.012041154,-0.017721452,-0.013294458,-0.009709738,-0.0023499455,0.01063961,0.011205617,0.0055994396,0.008072358,0.004005857,0.02045716,-0.0001461346,0.019203855,-0.03123827,-0.02530866,0.03703312,0.026979731,0.0035173376,0.00048346547,-0.002422381,0.002232027,0.011731197,-0.02373192,-0.0034381638,-0.02258643,0.0009660887,-0.021966515,0.033044107,-0.017964028,-0.0062395683,0.01526875,-0.020349348,0.023799304,-0.0143119255,-0.0062766285,-0.007412014,0.00038218198,0.0064989887,-0.007418752,0.017707976,0.03183123,0.03239724,0.00060685864,-0.0003868145,0.0009012335,0.014567977,0.01246566,-0.01959467,0.01003991,-0.027976984,0.0066943965,-0.010181412,-0.0032191726,0.0052793757,-0.015349608,-0.02216866,0.041480325,0.01406935,0.021953039,-0.020821022,-0.00083090365,-0.01925776,-0.018866945,-0.009002228,-0.031076554,0.002766029,0.020470636,-0.005882444,-0.020645829,-0.6201295,-0.009986005,0.016131239,-0.00020519909,0.006377701,0.023152437,0.0016020058,0.0011092753,-0.028003937,0.009487378,-0.028542994,0.019217331,-0.005680298,0.0057678944,0.0026666406,-0.035254236,-0.0028233037,0.009345876,0.00515135,-0.008853988,-0.025578188,0.014190638,0.0085372925,-0.0235163,-0.007620898,0.016508577,0.0025554604,-0.013665059,0.0054848907,-0.0051412424,-0.0006009627,0.0016719146,0.0075467783,0.003172005,0.058649246,-0.018260507,0.005730835,0.024324883,0.00021625393,0.032424193,-0.026777586,-0.01892085,0.01925776,-0.016818535,0.0023651063,-0.00470326,0.030214066,0.014028922,-0.02105012,-0.0016205359,0.02138703,-0.004881822,-0.010275747,-0.019500336,0.010134245,-0.02978282,0.028839473,0.0060037314,0.003631887,0.031399988,0.00081953296,0.009952313,-0.037572175,-0.0072166068,0.017115014,0.009736692,-0.022748146,0.0412108,0.01112476,-0.0135842,0.007398538,-0.001080638,-0.0186648,0.0116368625,-0.0019961903,0.03131913,0.000084016865,-0.0368714,-0.0019355464,-0.000053115924,-0.014352355,-0.0014520809,-0.00095682364,-0.021292696,0.011387549,0.0010932721,-0.024527028,-0.009123515,0.02503913,-0.0050368006,0.012344372,0.024163166,0.000032532847,-0.02851604,0.026319388,0.00533665,-0.024230547,0.002112424,0.0068325293,-0.023907116,0.011259523,-0.00940652,0.01590214,0.00028405702,0.02482351,0.0037767582,-0.010545275,-0.01668377,0.017074585,-0.029189859,-0.0038643547,0.005919504,0.000663291,-0.014298449,0.0011168558,-0.031399988,0.011057378,0.009723215,0.016710723,-0.05422899,0.007823044,0.0095480215,0.030591404,-0.037814748,-0.01590214,0.02604986,-0.007236821,-0.0110371625,0.004474161,0.0007748924,-0.0032326488,-0.027276212,0.030429687,-0.02258643,0.019837245,0.012580209,0.023583682,-0.00027795054,0.012849737,-0.027424453,-0.00884725,-0.03317887,0.010370081,0.012485875,-0.014608406,-0.023341106,-0.0106193945,0.0005950668,-0.01355051,0.017519306,-0.008577722,0.010882185,-0.02489089,0.009783858,0.016737675,-0.019217331,-0.018341366,-0.016562482,-0.03075312,-0.0047942256,0.0013535349,0.01526875,-0.023435442,-0.021670034,0.0032579172,-0.009911885,0.0065933233,0.018839993,-0.015982999,-0.024836985,0.014945316,-0.0118726995,-0.000816585,0.000528106,0.0012162442,0.00746592,0.014379308,-0.002981651,0.015592183,-0.0049289893,0.0069133877,-0.028246513,-0.016818535,-0.00059085543,0.028920332,-0.0033168762,-0.000254788,0.026359817,-0.0034465867,0.01881304,-0.037006166,0.008159954,-0.011987248,-0.010835017,-0.0010031487,0.018718705,0.007917379,0.016912868,0.031265225,0.010289223,0.00789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want to post and get paid how does this 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somewhere the requirements for a post. For example how the bounty comes and what needs to happen for that. 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want to know if I am submitted in winning the trip to Aruba since I ordered the vitamin 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I uploaded a tik tok for the Sacheu lip liners but it’s still asking on my account if I’m ready to post. 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gitextract_bz2543u5/ ├── .eslintrc.js ├── .gitignore ├── .npmignore ├── .nvmrc ├── .prettierrc ├── LICENSE ├── README.md ├── jest.config.js ├── package.json ├── scripts/ │ └── demo.js ├── src/ │ ├── index.ts │ ├── sample-data.json │ ├── types.ts │ ├── utils.ts │ ├── vector2Trend.ts │ └── vector2trends.test.ts └── tsconfig.json
SYMBOL INDEX (14 symbols across 3 files)
FILE: src/types.ts
type DataPoint (line 1) | interface DataPoint {
type ClusteringArgument (line 7) | type ClusteringArgument = {
type ClusterRankingGroup (line 15) | type ClusterRankingGroup = {
type ClusteringResult (line 24) | type ClusteringResult = {
type ClassificationRequest (line 28) | type ClassificationRequest = {
type ClassifiedClusterResponse (line 37) | type ClassifiedClusterResponse = {
FILE: src/utils.ts
function euclideanDistance (line 5) | function euclideanDistance(point1: number[], point2: number[]): number {
function averageDistance (line 17) | function averageDistance(point: number[], points: number[][]): number {
function silhouetteScore (line 28) | function silhouetteScore(
function createCustomClusterRankings (line 75) | function createCustomClusterRankings(
FILE: src/vector2Trend.ts
class Vector2Trend (line 158) | class Vector2Trend {
method cluster (line 159) | static cluster(args: ClusteringArgument): ClusteringResult {
method classify (line 221) | static async classify(
method enableLogging (line 227) | static enableLogging() {
Condensed preview — 17 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,672K chars).
[
{
"path": ".eslintrc.js",
"chars": 251,
"preview": "module.exports = {\n root: true,\n parser: '@typescript-eslint/parser',\n plugins: ['@typescript-eslint'],\n extends: [\n"
},
{
"path": ".gitignore",
"chars": 2047,
"preview": "# Logs\nlogs\n*.log\nnpm-debug.log*\nyarn-debug.log*\nyarn-error.log*\nlerna-debug.log*\n.pnpm-debug.log*\n\n# Diagnostic reports"
},
{
"path": ".npmignore",
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"preview": "src/\n*.test.ts\n"
},
{
"path": ".nvmrc",
"chars": 8,
"preview": "v16.15.0"
},
{
"path": ".prettierrc",
"chars": 105,
"preview": "{\n \"semi\": true,\n \"trailingComma\": \"all\",\n \"singleQuote\": true,\n \"printWidth\": 80,\n \"tabWidth\": 2\n}\n"
},
{
"path": "LICENSE",
"chars": 1069,
"preview": "MIT License\n\nCopyright (c) 2023 Aaron Decker\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "README.md",
"chars": 3934,
"preview": "# vector-2-trend\n\nTake (text) vectors and get out trending topics using clustering algorithms.\n\n```\nnpm i vector-2-trend"
},
{
"path": "jest.config.js",
"chars": 227,
"preview": "module.exports = {\n roots: ['<rootDir>/src'],\n transform: {\n '^.+\\\\.tsx?$': 'ts-jest',\n },\n testRegex: '(/__tests"
},
{
"path": "package.json",
"chars": 1238,
"preview": "{\n \"name\": \"vector-2-trend\",\n \"version\": \"1.0.0\",\n \"description\": \"take semantic text vectors, run clustering, classi"
},
{
"path": "scripts/demo.js",
"chars": 748,
"preview": "const Vector2Trends = require('../dist/index');\nconst fs = require('fs');\n\nconst testData = fs.readFileSync('./src/sampl"
},
{
"path": "src/index.ts",
"chars": 103,
"preview": "import { Vector2Trend } from \"./vector2Trend\";\n\nexport default Vector2Trend;\n\nexport * from \"./types\";\n"
},
{
"path": "src/sample-data.json",
"chars": 3645045,
"preview": "[{\"id\":\"c71a0b10-4330-4533-b78d-9dba3c194996\",\"text\":\"I'm Lancelot of Inner Circle ⭕️ \\\"The bad boys of Reggae\\\" are you"
},
{
"path": "src/types.ts",
"chars": 1009,
"preview": "export interface DataPoint {\n id: number | string;\n text: string;\n vector: number[];\n}\n\nexport type ClusteringArgumen"
},
{
"path": "src/utils.ts",
"chars": 3283,
"preview": "import { KMeansResult } from 'ml-kmeans/lib/KMeansResult';\nimport { ClusterRankingGroup, DataPoint } from './types';\nimp"
},
{
"path": "src/vector2Trend.ts",
"chars": 6167,
"preview": "import {\n ClassificationRequest,\n ClassifiedClusterResponse,\n ClusteringArgument,\n ClusteringResult,\n DataPoint,\n} "
},
{
"path": "src/vector2trends.test.ts",
"chars": 1264,
"preview": "import { ClusteringArgument, DataPoint } from './types';\nimport { Vector2Trend } from './vector2Trend';\nimport fs from '"
},
{
"path": "tsconfig.json",
"chars": 1311,
"preview": "{\n \"compilerOptions\": {\n \"target\": \"es6\" /* Specify ECMAScript target version: 'ES3' (default), 'ES5', 'ES6'/'ES2015"
}
]
About this extraction
This page contains the full source code of the a-r-d/vector-2-trend GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 17 files (3.5 MB), approximately 917.7k tokens, and a symbol index with 14 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.