Full Code of neuml/txtai.js for AI

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"]]
}


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FILE: .gitignore
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dist
node_modules
package-lock.json


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
<p align="center">
    <img src="https://raw.githubusercontent.com/neuml/txtai/master/logo.png"/>
</p>

<p align="center">
    <b>JavaScript client for txtai</b>
</p>

<p align="center">
    <a href="https://github.com/neuml/txtai.js/releases">
        <img src="https://img.shields.io/github/release/neuml/txtai.js.svg?style=flat&color=success" alt="Version"/>
    </a>
    <a href="https://github.com/neuml/txtai.js/releases">
        <img src="https://img.shields.io/github/release-date/neuml/txtai.js.svg?style=flat&color=blue" alt="GitHub Release Date"/>
    </a>
    <a href="https://github.com/neuml/txtai.js/issues">
        <img src="https://img.shields.io/github/issues/neuml/txtai.js.svg?style=flat&color=success" alt="GitHub Issues"/>
    </a>
    <a href="https://github.com/neuml/txtai.js">
        <img src="https://img.shields.io/github/last-commit/neuml/txtai.js.svg?style=flat&color=blue" alt="GitHub Last Commit"/>
    </a>
</p>

[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
```


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FILE: examples/node/.babelrc
================================================
{
    "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;