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<!DOCTYPE html>
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<h1 align="center">
<strong>ALI</strong>
</h1>
<p align="center">
Asistente Legal Inteligente
</p>
<div data-align="center">
<p><img src="https://img.shields.io/badge/huggingface-transformers-blue.png" class="img-fluid"> <img src="https://img.shields.io/badge/python-3.10-blue" class="img-fluid"> <img src="https://img.shields.io/badge/Angular-DD0031?style=flat&logo=Angular.png" class="img-fluid"> <img src="https://img.shields.io/badge/GenAI-blue.png" class="img-fluid"> <img src="https://img.shields.io/badge/Made%20with-Love-red.png" class="img-fluid"></p>
</div>
<p align="center">
<img src="./ali_logo.png" alt="readme_image" style="width:220px;height:220px;">
</p>
<p>This repo contains the code and instructions to set up ALI, a web UI and back-end pipeline for Retrieval Augmented Generation around legal documents. You can try ALI <a href="https://ali.sandboxai.ar/">here</a> .</p>
<section id="table-of-contents" class="level2">
<h2 class="anchored" data-anchor-id="table-of-contents">Table of Contents</h2>
<ul>
<li><a href="#installation">Installation</a></li>
<li><a href="#description">Description</a>
<ul>
<li><a href="#overview">Overview</a></li>
<li><a href="#technical-information">Technical information</a>
<ul>
<li><a href="#general">General</a></li>
<li><a href="#on-llm-frameworks">On LLM frameworks</a></li>
<li><a href="#main-challenges-encountered">Main challenges encountered</a></li>
<li><a href="#improvements-over-baseline-rag">Improvements over baseline RAG.</a></li>
<li><a href="#dataset">Dataset</a></li>
<li><a href="#testing">Testing</a></li>
</ul></li>
</ul></li>
<li><a href="#acknowledgements">Acknowledgementes</a></li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#sponsoring-the-project">Sponsoring the project</a></li>
<li><a href="#license">License</a></li>
</ul>
<section id="installation" class="level3">
<h3 class="anchored" data-anchor-id="installation">Installation</h3>
<p>Clone the repo, create a new environment and install backend and frontend requirements:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">git</span> clone https://github.com/sandbox-ai/ali.git</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> ali</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> create <span class="at">--name</span> ali</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> activate ali</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> backend</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="at">-r</span> requirements.txt</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> ../frontend</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="ex">npm</span> install </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p><strong>Usage</strong></p>
<p>Launch the backend in one terminal:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> activate ali</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="bu">export</span> <span class="va">OPENAI_API_KEY</span><span class="op">=<</span>your-key-here<span class="op">></span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co"># or if you have your own LLM backend</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="bu">export</span> <span class="va">OPENAI_API_BASE</span><span class="op">=<</span>custom-endpoint<span class="op">></span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> backend</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> api.py</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Open a new terminal and launch the frontend:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> frontend</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="ex">ng</span> serve</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Also see <a href="backend/README.md"><code>backend/README.md</code></a> and <a href="frontend/README.md"><code>frontend/README.md</code></a></p>
</section>
</section>
<section id="description" class="level2">
<h2 class="anchored" data-anchor-id="description">Description</h2>
<section id="overview" class="level3">
<h3 class="anchored" data-anchor-id="overview">Overview</h3>
<p>The complexity of legal documents and legalese terminology presents a barrier to most of the population, who won’t able to interact and understand their own legal system unless aided by a professional in the field.</p>
<p>To help close this gap in the Spanish speaking world, we built the Asistente Legal Inteligente (or ALI). It uses Retrieval Augmented Generation (<a href="https://arxiv.org/abs/2005.11401">RAG</a>), i.e. searching a vectorstore given an user query and formulating an answer with a Large Language Model (LLM), and a custom dataset that lays a RAG optimized structure.</p>
<p>The result is a grounded and comprehensive assistant that can answer questions about general legislation and specific legal situations.</p>
</section>
<section id="technical-information" class="level3">
<h3 class="anchored" data-anchor-id="technical-information">Technical information</h3>
<section id="general" class="level4">
<h4 class="anchored" data-anchor-id="general">General</h4>
<p>The user query is embedded using a custom Spanish <a href="https://huggingface.co/dariolopez/roberta-base-bne-finetuned-msmarco-qa-es-mnrl-mn">embedding model</a>, and then used to search for the best matching legal documents with cosine-similarity. To formulate the answer, an LLM hosted with an <a href="https://platform.openai.com/docs/api-reference">OpenAI compatible API endpoint</a> is queried with a custom prompt and the relevant documents.</p>
<p>This technique has ample room for improvements. See our roadmap on <a href="#improvements-over-baseline-rag">RAG improvements</a>. You can check out the RAG system written from scratch in <a href="src/rag_session.py"><code>src/rag_session.py</code></a></p>
</section>
<section id="on-llm-frameworks" class="level4">
<h4 class="anchored" data-anchor-id="on-llm-frameworks">On LLM frameworks</h4>
<p>We’ve tested both <a href="https://github.com/run-llama/llama_index">llama_index</a> and <a href="https://github.com/langchain-ai/langchain">langchain</a>, but found them too restrictive and in the end more cumbersome than developing our own pipeline over <a href="https://huggingface.co/docs/transformers/en/index">transformers</a>, enabling finer control and suprevision.</p>
</section>
<section id="main-challenges-encountered" class="level4">
<h4 class="anchored" data-anchor-id="main-challenges-encountered">Main challenges encountered</h4>
<p>The main problem we encountered with a RAG pipeline over Argentinian legal data was the embedding of the information. This problem has two parts:</p>
<ol type="1">
<li><p>Embedding model:</p>
<p>We tested OpenAI’s embedding model (ada-002), and found that it wasn’t great at distancing different legal topics and clustering similar ones (and it wasn’t great in general for Spanish). Thus we opted for the custom embedding <a href="https://huggingface.co/dariolopez/roberta-base-bne-finetuned-msmarco-qa-es-mnrl-mn">model</a> described earlier.</p>
<p><strong>Notes:</strong> Newer models that are topping the <a href="https://huggingface.co/spaces/mteb/leaderboard">MTEB Leaderboard</a> are worth testing. New OpenAI’s <a href="https://openai.com/blog/new-embedding-models-and-api-updates?ref=blog.salesforceairesearch.com">embbeding models</a> have been released and should be tested.</p></li>
<li><p>Legal Document Chunking/Parsing</p>
<p>We found that naively chunking legal documents with a regular document chunker was sub-par, trimming and leaving out vital contextual information. We decided to define each legal article as our atomic unit and embbed it as a whole.</p></li>
</ol>
<p>When we tried to embed full articles as they were, distancing and clustering of vectors wasn’t great. The trick that did it was to prepend all the contextual metadata to each article BEFORE embedding. So, each article to be embbeded would look like:</p>
<pre><code>"Decreto de Necesidad y Urgencia N° DNU-2023-70-APN-PTE. Fecha 20-12-2023. Titulo II: DESREGULACIÓN ECONÓMICA. Capitulo II: Tarjetas de crédito (Ley N° 25.065). Articulo 15: La entidad emisora deberá obligatoriamente dar a conocer el público la [...]"</code></pre>
<p>instead of just</p>
<pre><code>"Articulo 15: La entidad emisora deberá obligatoriamente dar a conocer el público la [...]".</code></pre>
<p>All the results we found in relation to the embedding models were a direct conclusion of plotting the resulting embedding vectors with the dimensionality reduction technique <a href="https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html">t-SNE: t-distributed Stochastic Neighbor Embedding</a>. Note that there are alternatives such as <a href="https://github.com/lmcinnes/umap">UMAP: Universal Manifold Approximation & Projection</a></p>
</section>
<section id="improvements-over-baseline-rag." class="level4">
<h4 class="anchored" data-anchor-id="improvements-over-baseline-rag.">Improvements over baseline RAG.</h4>
<p>There are many improvements to be made over this baseline RAG. The following is a non-exhaustive list:</p>
<ul>
<li>Query rewriting (hard without proper context of argentinean law, either parametric or non parametric), potentially connected with a Fusion retriever.</li>
<li>Document reranking</li>
<li>Hybrid Search (keyword-based search algorithms combined with vector search)</li>
<li>Linear adapter for the embedding model</li>
<li>Fine-tuning over each legal domain</li>
<li>Adding an agentic layer capable of handling complex questions through multiple hops of reasoning and retrieval.</li>
</ul>
</section>
<section id="dataset" class="level4">
<h4 class="anchored" data-anchor-id="dataset">Dataset</h4>
<p>For testing purposes, we include in the current repo a processed version of the <a href="https://www.argentina.gob.ar/normativa/nacional/decreto-70-2023-395521/texto">DNU 70/2023</a>, an extensive executive order that modifies a wide array of laws.</p>
<p>In order to create a vector database of the whole Argentinian law, we can look into the Boletín Oficial, where everything about legislation is published.</p>
<p>We have built <a href="https://github.com/sandbox-ai/Boletin-Oficial-Argentina">a tool to scrap</a> the whole Boletín Oficial into a dataset. You can also find the result uploaded and up-to-date on <a href="https://huggingface.co/datasets/marianbasti/boletin-oficial-argentina">Huggingface</a>.</p>
<p>This raw dataset must be parsed into the format described earlier (prepending contextual metadata). Given the inconsistent and unpredictable formatting of the documents and texts, there is no simple programmatic parsing to automate the process. We found that various NLP techniques are useful in automating this task (prompting LLMs, sentence-transformers, NER).</p>
</section>
<section id="testing" class="level4">
<h4 class="anchored" data-anchor-id="testing">Testing</h4>
<p><a href="https://github.com/explodinggradients/ragas">Ragas</a> is a framework to evaluate RAG pipelines that could be used to test ALI. It is important to acknowledge the costs using a paid API (we tested this with a relatively small document and GPT-4 and spent 40 USD in half an hour!)</p>
</section>
</section>
</section>
<section id="acknowledgements" class="level2">
<h2 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h2>
<p>Huge thanks to the <a href="https://github.com/bukosabino/justicio">Justicio</a> team from Spain, who gave us a lot of tips and shared their embedding model with us Definetly go check their project and talk to the creators!</p>
</section>
<section id="contributing" class="level2">
<h2 class="anchored" data-anchor-id="contributing">Contributing</h2>
<ol type="1">
<li>Fork it!</li>
<li>Create your feature branch: <code>git checkout -b my-new-feature</code></li>
<li>Commit your changes: <code>git commit -am 'Add some feature'</code></li>
<li>Push to the branch: <code>git push origin my-new-feature</code></li>
<li>Submit a pull request :D</li>
</ol>
<p>Please refer to <a href="CONTRIBUTING.md">CONTRIBUTING.md</a> for a detailed explanation of branch/commit naming conventions</p>
</section>
<section id="who-we-are" class="level2">
<h2 class="anchored" data-anchor-id="who-we-are">Who we are</h2>
<p>We are a group of Argentinian developers named <a href="https://sandbox-ai.github.io/"><code>sandbox.ai</code></a>. You can find us on <a href="https://twitter.com/sandboxaiorg">Twitter</a> and <a href="https://www.linkedin.com/in/sandboxai-org-b0a7842b4/">LinkedIn</a>. You can also contact us directly at <code>sandboxai.org@proton.me</code>.</p>
</section>
<section id="sponsoring-the-project" class="level2">
<h2 class="anchored" data-anchor-id="sponsoring-the-project">Sponsoring the project</h2>
<p>As of now, ALI is running on AWS using our OpenAI API key. Both things are expensive and as of now these costs are being covered by the sandbox.ai devs. If you find this project useful and would like to sponsor us (either by covering the AWS costs or the OpenAI api key costs), please contact us at <code>sandboxai.org@proton.me</code>.</p>
</section>
<section id="license" class="level2">
<h2 class="anchored" data-anchor-id="license">License</h2>
<p>GPL</p>
</section>
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const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
if (bsSheetEl) {
toggleBodyColorMode(bsSheetEl);
}
}
toggleBodyColorPrimary();
const icon = "";
const anchorJS = new window.AnchorJS();
anchorJS.options = {
placement: 'right',
icon: icon
};
anchorJS.add('.anchored');
const isCodeAnnotation = (el) => {
for (const clz of el.classList) {
if (clz.startsWith('code-annotation-')) {
return true;
}
}
return false;
}
const onCopySuccess = function(e) {
// button target
const button = e.trigger;
// don't keep focus
button.blur();
// flash "checked"
button.classList.add('code-copy-button-checked');
var currentTitle = button.getAttribute("title");
button.setAttribute("title", "Copied!");
let tooltip;
if (window.bootstrap) {
button.setAttribute("data-bs-toggle", "tooltip");
button.setAttribute("data-bs-placement", "left");
button.setAttribute("data-bs-title", "Copied!");
tooltip = new bootstrap.Tooltip(button,
{ trigger: "manual",
customClass: "code-copy-button-tooltip",
offset: [0, -8]});
tooltip.show();
}
setTimeout(function() {
if (tooltip) {
tooltip.hide();
button.removeAttribute("data-bs-title");
button.removeAttribute("data-bs-toggle");
button.removeAttribute("data-bs-placement");
}
button.setAttribute("title", currentTitle);
button.classList.remove('code-copy-button-checked');
}, 1000);
// clear code selection
e.clearSelection();
}
const getTextToCopy = function(trigger) {
const codeEl = trigger.previousElementSibling.cloneNode(true);
for (const childEl of codeEl.children) {
if (isCodeAnnotation(childEl)) {
childEl.remove();
}
}
return codeEl.innerText;
}
const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
text: getTextToCopy
});
clipboard.on('success', onCopySuccess);
if (window.document.getElementById('quarto-embedded-source-code-modal')) {
// For code content inside modals, clipBoardJS needs to be initialized with a container option
// TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
text: getTextToCopy,
container: window.document.getElementById('quarto-embedded-source-code-modal')
});
clipboardModal.on('success', onCopySuccess);
}
var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
var mailtoRegex = new RegExp(/^mailto:/);
var filterRegex = new RegExp('/' + window.location.host + '/');
var isInternal = (href) => {
return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
}
// Inspect non-navigation links and adorn them if external
var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
for (var i=0; i<links.length; i++) {
const link = links[i];
if (!isInternal(link.href)) {
// undo the damage that might have been done by quarto-nav.js in the case of
// links that we want to consider external
if (link.dataset.originalHref !== undefined) {
link.href = link.dataset.originalHref;
}
}
}
function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
const config = {
allowHTML: true,
maxWidth: 500,
delay: 100,
arrow: false,
appendTo: function(el) {
return el.parentElement;
},
interactive: true,
interactiveBorder: 10,
theme: 'quarto',
placement: 'bottom-start',
};
if (contentFn) {
config.content = contentFn;
}
if (onTriggerFn) {
config.onTrigger = onTriggerFn;
}
if (onUntriggerFn) {
config.onUntrigger = onUntriggerFn;
}
window.tippy(el, config);
}
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
for (var i=0; i<noterefs.length; i++) {
const ref = noterefs[i];
tippyHover(ref, function() {
// use id or data attribute instead here
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
try { href = new URL(href).hash; } catch {}
const id = href.replace(/^#\/?/, "");
const note = window.document.getElementById(id);
if (note) {
return note.innerHTML;
} else {
return "";
}
});
}
const xrefs = window.document.querySelectorAll('a.quarto-xref');
const processXRef = (id, note) => {
// Strip column container classes
const stripColumnClz = (el) => {
el.classList.remove("page-full", "page-columns");
if (el.children) {
for (const child of el.children) {
stripColumnClz(child);
}
}
}
stripColumnClz(note)
if (id === null || id.startsWith('sec-')) {
// Special case sections, only their first couple elements
const container = document.createElement("div");
if (note.children && note.children.length > 2) {
container.appendChild(note.children[0].cloneNode(true));
for (let i = 1; i < note.children.length; i++) {
const child = note.children[i];
if (child.tagName === "P" && child.innerText === "") {
continue;
} else {
container.appendChild(child.cloneNode(true));
break;
}
}
if (window.Quarto?.typesetMath) {
window.Quarto.typesetMath(container);
}
return container.innerHTML
} else {
if (window.Quarto?.typesetMath) {
window.Quarto.typesetMath(note);
}
return note.innerHTML;
}
} else {
// Remove any anchor links if they are present
const anchorLink = note.querySelector('a.anchorjs-link');
if (anchorLink) {
anchorLink.remove();
}
if (window.Quarto?.typesetMath) {
window.Quarto.typesetMath(note);
}
// TODO in 1.5, we should make sure this works without a callout special case
if (note.classList.contains("callout")) {
return note.outerHTML;
} else {
return note.innerHTML;
}
}
}
for (var i=0; i<xrefs.length; i++) {
const xref = xrefs[i];
tippyHover(xref, undefined, function(instance) {
instance.disable();
let url = xref.getAttribute('href');
let hash = undefined;
if (url.startsWith('#')) {
hash = url;
} else {
try { hash = new URL(url).hash; } catch {}
}
if (hash) {
const id = hash.replace(/^#\/?/, "");
const note = window.document.getElementById(id);
if (note !== null) {
try {
const html = processXRef(id, note.cloneNode(true));
instance.setContent(html);
} finally {
instance.enable();
instance.show();
}
} else {
// See if we can fetch this
fetch(url.split('#')[0])
.then(res => res.text())
.then(html => {
const parser = new DOMParser();
const htmlDoc = parser.parseFromString(html, "text/html");
const note = htmlDoc.getElementById(id);
if (note !== null) {
const html = processXRef(id, note);
instance.setContent(html);
}
}).finally(() => {
instance.enable();
instance.show();
});
}
} else {
// See if we can fetch a full url (with no hash to target)
// This is a special case and we should probably do some content thinning / targeting
fetch(url)
.then(res => res.text())
.then(html => {
const parser = new DOMParser();
const htmlDoc = parser.parseFromString(html, "text/html");
const note = htmlDoc.querySelector('main.content');
if (note !== null) {
// This should only happen for chapter cross references
// (since there is no id in the URL)
// remove the first header
if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
note.children[0].remove();
}
const html = processXRef(null, note);
instance.setContent(html);
}
}).finally(() => {
instance.enable();
instance.show();
});
}
}, function(instance) {
});
}
let selectedAnnoteEl;
const selectorForAnnotation = ( cell, annotation) => {
let cellAttr = 'data-code-cell="' + cell + '"';
let lineAttr = 'data-code-annotation="' + annotation + '"';
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
return selector;
}
const selectCodeLines = (annoteEl) => {
const doc = window.document;
const targetCell = annoteEl.getAttribute("data-target-cell");
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
const lineIds = lines.map((line) => {
return targetCell + "-" + line;
})
let top = null;
let height = null;
let parent = null;
if (lineIds.length > 0) {
//compute the position of the single el (top and bottom and make a div)
const el = window.document.getElementById(lineIds[0]);
top = el.offsetTop;
height = el.offsetHeight;
parent = el.parentElement.parentElement;
if (lineIds.length > 1) {
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
height = bottom - top;
}
if (top !== null && height !== null && parent !== null) {
// cook up a div (if necessary) and position it
let div = window.document.getElementById("code-annotation-line-highlight");
if (div === null) {
div = window.document.createElement("div");
div.setAttribute("id", "code-annotation-line-highlight");
div.style.position = 'absolute';
parent.appendChild(div);
}
div.style.top = top - 2 + "px";
div.style.height = height + 4 + "px";
div.style.left = 0;
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
if (gutterDiv === null) {
gutterDiv = window.document.createElement("div");
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
gutterDiv.style.position = 'absolute';
const codeCell = window.document.getElementById(targetCell);
const gutter = codeCell.querySelector('.code-annotation-gutter');
gutter.appendChild(gutterDiv);
}
gutterDiv.style.top = top - 2 + "px";
gutterDiv.style.height = height + 4 + "px";
}
selectedAnnoteEl = annoteEl;
}
};
const unselectCodeLines = () => {
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
elementsIds.forEach((elId) => {
const div = window.document.getElementById(elId);
if (div) {
div.remove();
}
});
selectedAnnoteEl = undefined;
};
// Handle positioning of the toggle
window.addEventListener(
"resize",
throttle(() => {
elRect = undefined;
if (selectedAnnoteEl) {
selectCodeLines(selectedAnnoteEl);
}
}, 10)
);
function throttle(fn, ms) {
let throttle = false;
let timer;
return (...args) => {
if(!throttle) { // first call gets through
fn.apply(this, args);
throttle = true;
} else { // all the others get throttled
if(timer) clearTimeout(timer); // cancel #2
timer = setTimeout(() => {
fn.apply(this, args);
timer = throttle = false;
}, ms);
}
};
}
// Attach click handler to the DT
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
for (const annoteDlNode of annoteDls) {
annoteDlNode.addEventListener('click', (event) => {
const clickedEl = event.target;
if (clickedEl !== selectedAnnoteEl) {
unselectCodeLines();
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
if (activeEl) {
activeEl.classList.remove('code-annotation-active');
}
selectCodeLines(clickedEl);
clickedEl.classList.add('code-annotation-active');
} else {
// Unselect the line
unselectCodeLines();
clickedEl.classList.remove('code-annotation-active');
}
});
}
const findCites = (el) => {
const parentEl = el.parentElement;
if (parentEl) {
const cites = parentEl.dataset.cites;
if (cites) {
return {
el,
cites: cites.split(' ')
};
} else {
return findCites(el.parentElement)
}
} else {
return undefined;
}
};
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
for (var i=0; i<bibliorefs.length; i++) {
const ref = bibliorefs[i];
const citeInfo = findCites(ref);
if (citeInfo) {
tippyHover(citeInfo.el, function() {
var popup = window.document.createElement('div');
citeInfo.cites.forEach(function(cite) {
var citeDiv = window.document.createElement('div');
citeDiv.classList.add('hanging-indent');
citeDiv.classList.add('csl-entry');
var biblioDiv = window.document.getElementById('ref-' + cite);
if (biblioDiv) {
citeDiv.innerHTML = biblioDiv.innerHTML;
}
popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
}
}
});
</script>
</div> <!-- /content -->
</body></html>