Repository: neuml/txtai.js
Branch: master
Commit: 85ea6616ac2a
Files: 23
Total size: 40.1 KB
Directory structure:
gitextract_kxx1s_px/
├── .babelrc
├── .gitignore
├── LICENSE
├── README.md
├── examples/
│ └── node/
│ ├── .babelrc
│ ├── package.json
│ └── src/
│ ├── embeddings.js
│ ├── extractor.js
│ ├── labels.js
│ └── pipelines.js
├── package.json
└── src/
├── api.js
├── embeddings.js
├── extractor.js
├── index.js
├── labels.js
├── segmentation.js
├── similarity.js
├── summary.js
├── textractor.js
├── transcription.js
├── translation.js
└── workflow.js
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FILE CONTENTS
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================================================
FILE: .babelrc
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{
"presets": ["@babel/preset-env"],
"plugins": [["@babel/transform-runtime"]]
}
================================================
FILE: .gitignore
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dist
node_modules
package-lock.json
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
JavaScript client for txtai
[txtai](https://github.com/neuml/txtai) is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.
This repository contains JavaScript bindings for the txtai API.
## Installation
txtai.js can be installed via npm
npm install txtai
## Examples
The examples directory has a series of examples that give an overview of txtai. See the list of examples below.
| Example | Description |
|:----------|:-------------|
| [Introducing txtai](https://github.com/neuml/txtai.js/blob/master/examples/node/src/embeddings.js) | Overview of the functionality provided by txtai |
| [Extractive QA with txtai](https://github.com/neuml/txtai.js/blob/master/examples/node/src/extractor.js) | Extractive question-answering with txtai |
| [Labeling with zero-shot classification](https://github.com/neuml/txtai.js/blob/master/examples/node/src/labels.js) | Labeling with zero-shot classification |
| [Pipelines and workflows](https://github.com/neuml/txtai.js/blob/master/examples/node/src/pipelines.js) | Pipelines and workflows |
txtai.js connects to a txtai api instance. See [this link](https://neuml.github.io/txtai/api/) for details on how to start a new api instance.
Once an api instance is running, do the following to run the examples.
```
git clone https://github.com/neuml/txtai.js
cd txtai.js/examples/node
npm install
npm run build
node dist/embeddings.js
node dist/extractor.js
node dist/labels.js
node dist/pipelines.js
```
================================================
FILE: examples/node/.babelrc
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{
"presets": ["@babel/preset-env"],
"plugins": [["@babel/transform-runtime"]]
}
================================================
FILE: examples/node/package.json
================================================
{
"name": "txtai-node",
"dependencies": {
"sprintf-js": ">=1.1.2",
"txtai": ">=9.9.0"
},
"devDependencies": {
"@babel/cli": ">=7.10.5",
"@babel/core": ">=7.10.5",
"@babel/node": ">=7.10.5",
"@babel/plugin-transform-runtime": ">=7.10.5",
"@babel/preset-env": ">=7.10.5"
},
"scripts": {
"build": "node_modules/@babel/cli/bin/babel.js src -d dist -s --verbose"
}
}
================================================
FILE: examples/node/src/embeddings.js
================================================
import {Embeddings} from "txtai";
import {sprintf} from "sprintf-js";
/**
* Example embeddings functionality.
*
* Implements functionality found in this notebook: https://github.com/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb
*/
const run = async () => {
try {
let embeddings = new Embeddings("http://localhost:8000");
let data = ["US tops 5 million confirmed virus cases",
"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
"Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
"The National Park Service warns against sacrificing slower friends in a bear attack",
"Maine man wins $1M from $25 lottery ticket",
"Make huge profits without work, earn up to $100,000 a day"];
console.log("Running similarity queries");
console.log(sprintf("%-20s %s", "Query", "Best Match"));
console.log("-".repeat(50));
for (let query of ["feel good story", "climate change", "public health story", "war", "wildlife", "asia", "lucky", "dishonest junk"]) {
let results = await embeddings.similarity(query, data);
let uid = results[0].id;
console.log(sprintf("%-20s %s", query, data[uid]))
}
let documents = data.map((text, index) => ({id: index, text: text}));
await embeddings.add(documents);
await embeddings.index();
console.log();
console.log("Building an Embeddings index");
console.log(sprintf("%-20s %s", "Query", "Best Match"));
console.log("-".repeat(50));
for (let query of ["feel good story", "climate change", "public health story", "war", "wildlife", "asia", "lucky", "dishonest junk"]) {
let results = await embeddings.search(query, 1);
let uid = results[0].id;
console.log(sprintf("%-20s %s", query, data[uid]));
}
data[0] = "See it: baby panda born"
await embeddings.delete([5]);
await embeddings.add([{id: 0, text: data[0]}])
await embeddings.upsert();
console.log();
console.log("Test delete/upsert/count");
console.log(sprintf("%-20s %s", "Query", "Best Match"));
console.log("-".repeat(50));
let query = "feel good story";
let results = await embeddings.search(query, 1);
let uid = results[0].id;
console.log(sprintf("%-20s %s", query, data[uid]));
let count = await embeddings.count();
console.log();
console.log(sprintf("Total Count: %d", count));
}
catch (e) {
console.trace(e);
}
};
run();
================================================
FILE: examples/node/src/extractor.js
================================================
import {Extractor} from "txtai";
/**
* Example extractor functionality.
*
* Implements logic found in this notebook: https://github.com/neuml/txtai/blob/master/examples/05_Extractive_QA_with_txtai.ipynb
*/
const run = async () => {
try {
let extractor = new Extractor("http://localhost:8000");
let data = ["Giants hit 3 HRs to down Dodgers",
"Giants 5 Dodgers 4 final",
"Dodgers drop Game 2 against the Giants, 5-4",
"Blue Jays beat Red Sox final score 2-1",
"Red Sox lost to the Blue Jays, 2-1",
"Blue Jays at Red Sox is over. Score: 2-1",
"Phillies win over the Braves, 5-0",
"Phillies 5 Braves 0 final",
"Final: Braves lose to the Phillies in the series opener, 5-0",
"Lightning goaltender pulled, lose to Flyers 4-1",
"Flyers 4 Lightning 1 final",
"Flyers win 4-1"]
// Run series of questions
let questions = ["What team won the game?", "What was score?"];
for (let query of ["Red Sox - Blue Jays", "Phillies - Braves", "Dodgers - Giants", "Flyers - Lightning"]) {
console.log("----" + query + "----");
let queue = questions.map(question => ({name: question, query: query, question: question, snippet: false}));
console.log(await extractor.extract(queue, data));
}
// Ad-hoc questions
let question = "What hockey team won?";
console.log("----" + question + "----");
console.log(await extractor.extract([{name: question, query: question, question: question, snippet: false}], data));
}
catch (e) {
console.trace(e);
}
};
run();
================================================
FILE: examples/node/src/labels.js
================================================
import {Labels} from "txtai";
import {sprintf} from "sprintf-js";
/**
* Example labels functionality.
*
* Implements logic found in this notebook: https://github.com/neuml/txtai/blob/master/examples/07_Apply_labels_with_zero_shot_classification.ipynb
*/
const run = async () => {
try {
let labels = new Labels("http://localhost:8000");
let data = ["Dodgers lose again, give up 3 HRs in a loss to the Giants",
"Giants 5 Cardinals 4 final in extra innings",
"Dodgers drop Game 2 against the Giants, 5-4",
"Flyers 4 Lightning 1 final. 45 saves for the Lightning.",
"Slashing, penalty, 2 minute power play coming up",
"What a stick save!",
"Leads the NFL in sacks with 9.5",
"UCF 38 Temple 13",
"With the 30 yard completion, down to the 10 yard line",
"Drains the 3pt shot!!, 0:15 remaining in the game",
"Intercepted! Drives down the court and shoots for the win",
"Massive dunk!!! they are now up by 15 with 2 minutes to go"];
// List of labels
let tags = ["Baseball", "Football", "Hockey", "Basketball"];
console.log(sprintf("%-75s %s", "Text", "Label"));
console.log("-".repeat(100));
for (let text of data) {
let label = await labels.label(text, tags);
label = tags[label[0].id];
console.log(sprintf("%-75s %s", text, label));
}
tags = ["😀", "😡"];
console.log();
console.log(sprintf("%-75s %s", "Text", "Label"));
console.log("-".repeat(100));
for (let text of data) {
let label = await labels.label(text, tags);
label = tags[label[0].id];
console.log(sprintf("%-75s %s", text, label));
}
}
catch (e) {
console.trace(e);
}
};
run();
================================================
FILE: examples/node/src/pipelines.js
================================================
import {Segmentation, Textractor, Summary, Transcription, Translation, Workflow} from "txtai";
/**
* Example pipeline and workflow functionality.
*
* Uses files from txtai unit tests: https://github.com/neuml/txtai/releases/download/v2.0.0/tests.tar.gz
*/
const run = async () => {
try {
let service = "http://localhost:8000"
let segment = new Segmentation(service);
let sentences = "This is a test. And another test.";
console.log("---- Segmented Text ----");
console.log(await segment.segment(sentences));
let textractor = new Textractor(service);
let text = await textractor.textract("/tmp/txtai/article.pdf")
console.log("\n---- Extracted Text ----");
console.log(text);
let summary = new Summary(service);
let summarytext = await summary.summary(text);
console.log("\n---- Summary Text ----");
console.log(summarytext);
let translate = new Translation(service);
let translation = await translate.translate(summarytext, "es");
console.log("\n---- Summary Text in Spanish ----");
console.log(translation);
let workflow = new Workflow(service);
let output = await workflow.workflow("sumspanish", ["file:///tmp/txtai/article.pdf"]);
console.log("\n---- Workflow [Extract Text->Summarize->Translate] ----");
console.log(output);
let transcribe = new Transcription(service);
let transcription = await transcribe.transcribe("/tmp/txtai/Make_huge_profits.wav")
console.log("\n---- Transcribed Text ----");
console.log(transcription);
}
catch (e) {
console.trace(e);
}
};
run();
================================================
FILE: package.json
================================================
{
"name": "txtai",
"version": "9.9.0",
"author": "NeuML",
"description": "JavaScript client for txtai",
"license": "Apache-2.0",
"keywords": ["search", "nlp", "machine-learning", "similarity"],
"repository": "neuml/txtai.js",
"main": "dist/index.js",
"files": ["dist"],
"dependencies": {
"core-js": ">=3.6.5",
"isomorphic-fetch": ">=2.2.1"
},
"devDependencies": {
"@babel/cli": ">=7.10.5",
"@babel/core": ">=7.10.5",
"@babel/node": ">=7.10.5",
"@babel/plugin-transform-runtime": ">=7.10.5",
"@babel/preset-env": ">=7.10.5"
},
"scripts": {
"build": "node_modules/@babel/cli/bin/babel.js src -d dist -s --verbose"
}
}
================================================
FILE: src/api.js
================================================
// Backwards compatibility
import "core-js/features/promise";
import "isomorphic-fetch";
/**
* Base class for interfacing with a remote txtai service via REST API calls.
*/
class API {
/**
* Creates an API instance.
*
* @param url api url
* @param token api token
*/
constructor(url, token) {
// API url
this.url = url ? url : process.env.TXTAI_API_URL
// API token
this.token = token ? token : process.env.TXTAI_API_TOKEN
}
/**
* Executes a get request.
*
* @param method api method
* @param params query parameters
* @return response
*/
async get(method, params) {
// Build URL
let url = `${this.url}/${method}`;
if (params) {
url += `?${new URLSearchParams(params)}`;
}
// Execute remote call
let res = await fetch(url, {headers: this.headers()});
// Validate response and return JSON
return res.ok ? await res.json() : Promise.reject(`${res.status} ${res.statusText}`);
}
/**
* Executes a post request.
*
* @param method api method
* @param params post parameters
* @return response
*/
async post(method, params) {
// Build URL
let url = `${this.url}/${method}`;
// Default headers
let defaults = {"Content-Type": "application/json"};
// Execute remote call
let res = await fetch(url, {method: "post",
body: JSON.stringify(params),
headers: this.headers(defaults)});
// Validate response and return JSON
return res.ok ? await res.json() : Promise.reject(`${res.status} ${res.statusText}`);
}
/**
* Executes a multipart form post request.
*
* @param method api method
* @param formData FormData object
* @return response
*/
async postForm(method, formData) {
// Build URL
let url = `${this.url}/${method}`;
// Execute remote call (no Content-Type header - browser sets it with boundary)
let res = await fetch(url, {method: "post",
body: formData,
headers: this.headers()});
// Validate response
return res.ok ? Promise.resolve() : Promise.reject(`${res.status} ${res.statusText}`);
}
/**
* Creates default HTTP headers.
*
* @param base base headers
* @return headers
*/
headers(base) {
let headers = base ? base : {};
if (this.token) {
headers["Authorization"] = "Bearer " + this.token;
}
return headers
}
}
export default API;
================================================
FILE: src/embeddings.js
================================================
import API from "./api";
/**
* txtai embeddings instance.
*/
class Embeddings extends API {
/**
* Finds documents in the embeddings model most similar to the input query. Returns
* a list of {id: value, score: value} sorted by highest score, where id is the
* document id in the embeddings model.
*
* @param query query text
* @param limit maximum results (defaults to 10)
* @param weights hybrid score weights, if applicable
* @param index index name, if applicable
* @return list of {id: value, score: value}
*/
async search(query, limit = 10, weights = null, index = null) {
let params = {
query: query,
limit: limit
}
if (weights != null) {
params["weights"] = weights
}
if (index != null) {
params["index"] = index
}
return await this.get("search", params).catch(e => {
throw(e);
});
}
/**
* Finds documents in the embeddings model most similar to the input queries. Returns
* a list of {id: value, score: value} sorted by highest score per query, where id is
* the document id in the embeddings model.
*
* @param queries queries text
* @param limit maximum results (defaults to 10)
* @param weights hybrid score weights, if applicable
* @param index index name, if applicable
* @return list of {id: value, score: value} per query
*/
async batchsearch(queries, limit = 10, weights = null, index = null) {
let params = {
queries: queries,
limit: limit
}
if (weights != null) {
params["weights"] = weights
}
if (index != null) {
params["index"] = index
}
return await this.post("batchsearch", params).catch (e => {
throw(e);
});
}
/**
* Adds a batch of documents for indexing.
*
* @param documents list of {id: value, text: value}
*/
async add(documents) {
await this.post("add", documents).catch(e => {
throw(e);
});
}
/**
* Builds an embeddings index for previously batched documents.
*/
async index() {
await this.get("index", null).catch(e => {
throw(e);
});
}
/**
* Runs an embeddings upsert operation for previously batched documents.
*/
async upsert() {
await this.get("upsert", null).catch(e => {
throw(e);
});
}
/**
* Deletes from an embeddings index. Returns list of ids deleted.
*
* @param ids list of ids to delete
* @return ids deleted
*/
async delete(ids) {
return await this.post("delete", ids).catch(e => {
throw(e);
});
}
/**
* Recreates this embeddings index using config. This method only works if document content storage is enabled.
*
* @param config new config
* @param func optional function to prepare content for indexing
*/
async reindex(config, func = null) {
let params = {
config: config,
}
if (func != null) {
params["function"] = func
}
await this.post("reindex", params).catch(e => {
throw(e);
});
}
/**
* Total number of elements in this embeddings index.
*
* @return number of elements in embeddings index
*/
async count() {
return await this.get("count", null).catch(e => {
throw(e);
});
}
/**
* Computes the similarity between query and list of text. Returns a list of
* {id: value, score: value} sorted by highest score, where id is the index
* in texts.
*
* @param query query text
* @param texts list of text
* @return list of {id: value, score: value}
*/
async similarity(query, texts) {
return await this.post("similarity", {query: query, texts: texts}).catch (e => {
throw(e);
});
}
/**
* Computes the similarity between list of queries and list of text. Returns a list
* of {id: value, score: value} sorted by highest score per query, where id is the
* index in texts.
*
* @param queries queries text
* @param texts list of text
* @return list of {id: value, score: value} per query
*/
async batchsimilarity(queries, texts) {
return await this.post("batchsimilarity", {queries: queries, texts: texts}).catch (e => {
throw(e);
});
}
/**
* Transforms text into an embeddings array.
*
* @param text input text
* @return embeddings array
*/
async transform(text) {
return await this.get("transform", {text: text}).catch(e => {
throw(e);
});
}
/**
* Transforms list of text into embeddings arrays.
*
* @param texts list of text
* @return embeddings array
*/
async batchtransform(texts) {
return await this.post("batchtransform", texts).catch(e => {
throw(e);
});
}
/**
* Adds a batch of binary objects for indexing.
*
* @param data list of binary data (Blob, File, or Buffer objects)
* @param uid list of corresponding ids (optional)
* @param field optional object field name
*/
async addobject(data, uid = null, field = null) {
let formData = new FormData();
for (let i = 0; i < data.length; i++) {
formData.append("data", data[i], `file${i}`);
}
if (uid != null) {
for (let id of uid) {
formData.append("uid", id);
}
}
if (field != null) {
formData.append("field", field);
}
await this.postForm("addobject", formData).catch(e => {
throw(e);
});
}
/**
* Adds a batch of images for indexing.
*
* @param data list of image data (Blob, File, or Buffer objects)
* @param uid list of corresponding ids
* @param field optional object field name
*/
async addimage(data, uid, field = null) {
let formData = new FormData();
for (let i = 0; i < data.length; i++) {
formData.append("data", data[i], data[i].name || `image${i}`);
}
if (uid != null) {
for (let id of uid) {
formData.append("uid", id);
}
}
if (field != null) {
formData.append("field", field);
}
await this.postForm("addimage", formData).catch(e => {
throw(e);
});
}
}
export default Embeddings;
================================================
FILE: src/extractor.js
================================================
import API from "./api";
/**
* txtai extractor instance.
*/
class Extractor extends API {
/**
* Extracts answers to input questions.
*
* @param queue list of {name: value, query: value, question: value, snippet: value}
* @param texts list of text
* @return list of {name: value, answer: value}
*/
async extract(queue, texts) {
return await this.post("extract", {queue: queue, texts: texts}).catch (e => {
throw(e);
});
}
}
export default Extractor;
================================================
FILE: src/index.js
================================================
import Embeddings from "./embeddings";
import Extractor from "./extractor";
import Labels from "./labels";
import Segmentation from "./segmentation";
import Similarity from "./similarity";
import Summary from "./summary";
import Textractor from "./textractor";
import Transcription from "./transcription";
import Translation from "./translation";
import Workflow from "./workflow";
export {
Embeddings,
Extractor,
Labels,
Segmentation,
Similarity,
Summary,
Textractor,
Transcription,
Translation,
Workflow
};
================================================
FILE: src/labels.js
================================================
import API from "./api";
/**
* txtai labels instance.
*/
class Labels extends API {
/**
* Applies a zero shot classifier to text using a list of labels. Returns a list of
* {id: value, score: value} sorted by highest score, where id is the index in labels.
*
* @param text input text
* @param labels list of labels
* @return list of {id: value, score: value} per text element
*/
async label(text, labels) {
return await this.post("label", {text: text, labels: labels}).catch (e => {
throw(e);
});
}
/**
* Applies a zero shot classifier to list of text using a list of labels. Returns a list of
* {id: value, score: value} sorted by highest score, where id is the index in labels per
* text element.
*
* @param texts list of texts
* @param labels list of labels
* @return list of {id: value score: value} per text element
*/
async batchlabel(texts, labels) {
return await this.post("batchlabel", {texts: texts, labels: labels}).catch (e => {
throw(e);
});
}
}
export default Labels;
================================================
FILE: src/segmentation.js
================================================
import API from "./api";
/**
* txtai segmentation instance.
*/
class Segmentation extends API {
/**
* Segments text into semantic units.
*
* @param text input text
* @return segmented text
*/
async segment(text) {
return await this.get("segment", {text: text}).catch (e => {
throw(e);
});
}
/**
* Segments text into semantic units.
*
* @param texts list of texts to segment
* @return list of segmented text
*/
async batchsegment(texts) {
return await this.post("batchsegment", texts).catch (e => {
throw(e);
});
}
}
export default Segmentation;
================================================
FILE: src/similarity.js
================================================
import API from "./api";
/**
* txtai similarity instance.
*/
class Similarity extends API {
/**
* Computes the similarity between query and list of text. Returns a list of
* {id: value, score: value} sorted by highest score, where id is the index
* in texts.
*
* @param query query text
* @param texts list of text
* @return list of {id: value, score: value}
*/
async similarity(query, texts) {
return await this.post("similarity", {query: query, texts: texts}).catch (e => {
throw(e);
});
}
/**
* Computes the similarity between list of queries and list of text. Returns a list
* of {id: value, score: value} sorted by highest score per query, where id is the
* index in texts.
*
* @param queries queries text
* @param texts list of text
* @return list of {id: value, score: value} per query
*/
async batchsimilarity(queries, texts) {
return await this.post("batchsimilarity", {queries: queries, texts: texts}).catch (e => {
throw(e);
});
}
}
export default Similarity;
================================================
FILE: src/summary.js
================================================
import API from "./api";
/**
* txtai summary instance.
*/
class Summary extends API {
/**
* Runs a summarization model against a block of text.
*
* @param text text to summarize
* @param minlength minimum length for summary
* @param maxlength maximum length for summary
* @return summary text
*/
async summary(text, minlength, maxlength) {
let params = {text: text}
if (minlength) {
params.minlength = minlength;
}
if (maxlength) {
params.maxlength = maxlength;
}
return await this.get("summary", params).catch (e => {
throw(e);
});
}
/**
* Runs a summarization model against a block of text.
*
* @param texts list of text to summarize
* @param minlength minimum length for summary
* @param maxlength maximum length for summary
* @return list of summary text
*/
async batchsummary(texts, minlength, maxlength) {
let params = {texts: texts}
if (minlength) {
params.minlength = minlength;
}
if (maxlength) {
params.maxlength = maxlength;
}
return await this.post("batchsummary", params).catch (e => {
throw(e);
});
}
}
export default Summary;
================================================
FILE: src/textractor.js
================================================
import API from "./api";
/**
* txtai textractor instance.
*/
class Textractor extends API {
/**
* Extracts text from a file at path.
*
* @param file file to extract text
* @return extracted text
*/
async textract(file) {
return await this.get("textract", {file: file}).catch (e => {
throw(e);
});
}
/**
* Extracts text from a file at path.
*
* @param files list of files to extract text
* @return list of extracted text
*/
async batchtextract(files) {
return await this.post("batchtextract", files).catch (e => {
throw(e);
});
}
}
export default Textractor;
================================================
FILE: src/transcription.js
================================================
import API from "./api";
/**
* txtai transcription instance.
*/
class Transcription extends API {
/**
* Transcribes audio files to text.
*
* @param file file to transcribe
* @return transcribed text
*/
async transcribe(file) {
return await this.get("transcribe", {file: file}).catch (e => {
throw(e);
});
}
/**
* Transcribes audio files to text.
*
* @param files list of files to transcribe
* @return list of transcribed text
*/
async batchtranscribe(files) {
return await this.post("batchtranscribe", files).catch (e => {
throw(e);
});
}
}
export default Transcription;
================================================
FILE: src/translation.js
================================================
import API from "./api";
/**
* txtai translation instance.
*/
class Translation extends API {
/**
* Translates text from source language into target language.
*
* @param text text to translate
* @param target target language code, defaults to "en"
* @param source source language code, detects language if not provided
* @return translated text
*/
async translate(text, target, source) {
let params = {text: text};
if (target) {
params.target = target;
}
if (source) {
params.source = source;
}
return await this.get("translate", params).catch (e => {
throw(e);
});
}
/**
* Translates text from source language into target language.
*
* @param texts list of text to translate
* @param target target language code, defaults to "en"
* @param source source language code, detects language if not provided
* @return list of translated text
*/
async batchtranslate(texts, target, source) {
let params = {texts: texts};
if (target) {
params.target = target;
}
if (source) {
params.source = source;
}
return await this.post("batchtranslate", files).catch (e => {
throw(e);
});
}
}
export default Translation;
================================================
FILE: src/workflow.js
================================================
import API from "./api";
/**
* txtai workflow instance.
*/
class Workflow extends API {
/**
* Executes a named workflow using elements as input.
*
* @param name workflow name
* @param elements list of elements to run through workflow
* @return list of processed elements
*/
async workflow(name, elements) {
return await this.post("workflow", {name: name, elements: elements}).catch (e => {
throw(e);
});
}
}
export default Workflow;