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#43 MemoRAG, RAG Agent, RAG Fusion, and more!
Artificial Intelligence   Latest   Machine Learning

#43 MemoRAG, RAG Agent, RAG Fusion, and more!

Towards AI Editorial Team
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October 3, 2024
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Last Updated on October 5, 2024 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, we are diving into different RAG approaches, programming tips, community discussions, and some fun collaboration opportunities. Dive in and enjoy the read!

What’s AI Weekly

This week, in my other newsletter, the High Learning Rate newsletter, we are sharing the 15 best tips for programming with LLMs. We dive into context, error handling, and more. Read the article here!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

In collaboration with Bright Data:

Learn How You Can Leverage Web Data To Power Your AI Use Cases

Artificial intelligence models, particularly large language models (LLMs), thrive on vast, diverse, and real-time datasets to improve their predictions, learning, and decision-making capabilities. However, traditional datasets are often too static or limited in scope to support the constantly evolving demands of AI systems. This is where web data plays a critical role.

Leading companies are leveraging web data to power their AI innovations and, most importantly, their existing workflows. Access data more efficiently, ethically, and elastically.

Learn how to leverage Bright Data for your AI workflows and use cases!

Learn AI Together Community section!

Featured Community post from the Discord

Fabiochiu has been sharing weekly AI news updates in our ai-news channel on Discord. NLPlanet shares a quick summary of the top AI news and research of the week. It’s our go-to space for quick weekly updates. Check it out in the ai-news Discord channel!

AI poll of the week!

It seems like the increments from research papers are far less than they used to be. This could be due to several reasons. Drakonchik__ rightly pointed out that there is an alarming increase in papers generated with GPT. Would you agree? Tell us in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Swangaaw4 is looking for a learning partner to study AI technologies. If you are also looking for a partner to study with, connect with them in the thread!

2. Shreesha1573 is looking for someone to research using the capabilities of O1 models and prompt engineering. If you are a plus user and would love to experiment with O1, reach out in the thread!

3. Abdurrahman01234 needs a partner to brainstorm ideas for their portfolio. If you have some interesting ideas to discuss, contact in the thread!

Meme of the week!

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TAI Curated section

Article of the week

Teaching RAG to “Remember”: How MemoRAG Enhances Question-Answering Through Memory by Florian June

This article explores MemoRAG, a model that enhances question-answering systems through memory mechanisms. It dives into MemoRAG’s architecture and functionality, illustrating how it effectively retains and utilizes information to improve response accuracy. With clear explanations and practical examples, this guide helps advance the understanding of memory-augmented models and applications in AI-driven question answering.

Our must-read articles

1. How to Build a Custom Optimizer in PyTorch: 5 Simple Steps by Benjamin Bodner

This comprehensive guide walks you through building a custom optimizer in PyTorch in just five simple steps. It covers the fundamental concepts of optimization and provides clear, step-by-step instructions to help you create an optimizer tailored to your specific needs. With practical examples and code snippets, this article is perfect for both beginners and experienced developers looking to deepen their understanding of PyTorch and enhance their machine-learning models.

2. Build a Reliable RAG Agent That Can Scrape Any Website!! by Gao Dalie

This article provides a step-by-step guide on building a reliable Retrieval-Augmented Generation (RAG) agent capable of scraping data from any website. It covers essential techniques and tools needed for effective web scraping, including handling various website structures and ensuring data accuracy. With practical examples and expert tips, this resource enhances web scraping skills and creates robust RAG agents for diverse applications.

3. How to Perform Hyperparameter Optimization in PyTorch Using Optuna by Benjamin Bodner

This article explores hyperparameter optimization in PyTorch using Optuna, with a focus on the pruning technique to enhance efficiency. It explains the importance of hyperparameter tuning in improving model performance and provides a step-by-step guide on implementing Optuna for effective optimization.

4. Not RAG, but RAG Fusion? Understanding Next-Gen Info Retrieval by Surya Maddula

This article dives into the concept of RAG Fusion, a next-generation approach to information retrieval that combines the strengths of Retrieval-Augmented Generation (RAG) with advanced retrieval techniques. It explains the underlying principles of RAG Fusion and its potential to enhance the accuracy and relevance of retrieved information.

If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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',*/ ]; var replaceText = { '': '', '': '', '
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