The data engineer’s solution to turning data into vector embeddings. Building LLM demos is cool, turning 1B user clicks and millions of documents into vectors is cooler.
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Let's make vector search less scary! 🎃
Halloween Vector Search Survey 🎃👻 This past year, our Vector DB Comparison Table has been the go-to for thousands of AI engineers building with vectors. Now, it's time to hear your opinion. What challenges are you facing? What are your favorite tools, approaches and algorithms when working with vectors? Share your insights and we’ll send you the report when it drops. It's gonna be Spook-tacular! 🎃 Link in the comments 👇👇
We've distilled our comprehensive guide on building robust recommender systems into a quick, engaging video. In just one minute, you'll learn: - How to tackle the cold start problem - The power of collaborative filtering - Pro tips for scaling your RecSys 🔗 Check out the video here: https://buff.ly/4dLe1ep 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3X1Cczr #RecommenderSystems #DataScience #ML
RAG Evaluation in 60 Seconds! 🚀 We've summarized our comprehensive article on Retrieval Augmented Generation (RAG) evaluation into a quick, informative video. In just one minute, you'll learn: - Why RAG evaluation matters - Our 4-level evaluation framework - Pro tips for optimizing your RAG system 🔗 Check out the video here: https://buff.ly/3WIisQ1 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3yPfQYC #AI #MachineLearning #RAG
We've distilled our popular article on optimizing recommender systems with sparse metadata into a quick, info-packed video. Learn how to: - Leverage dummy nodes for better recommendations - Increase precision by up to 20% - Implement the technique with minimal code Watch now for a rapid-fire explanation of this powerful technique. Your recommender system will thank you! 🔗 Check out the video here: https://buff.ly/3WKOsDl 📚 For a deeper dive, read the comprehensive article: https://buff.ly/4fHI2gS #DataScience #MachineLearning #RecommenderSystems
We've distilled our comprehensive article on Retrieval Augmented Generation (RAG) into a quick, informative video. In just one minute, you'll learn: - What RAG is and why it's a game-changer - How it reduces AI hallucinations - Key applications in legal, finance, and healthcare - Pro tips for implementation 🔗 Check out the video here: https://buff.ly/3YEy6yA 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3X1Cqqh #AI #MachineLearning #RAG
𝐇𝐞𝐲 𝐦𝐚𝐫𝐤𝐞𝐭𝐞𝐫𝐬, 𝐡𝐞𝐫𝐞’𝐬 𝐚 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐭𝐢𝐩 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 𝐀𝐝𝐬 𝐜𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬. 💡 What if you had an AI tool that digs into your data to uncover the exact keywords that drive more clicks and sales? 👇 Here’s how it works in 4 easy steps: 1️⃣ 𝐏𝐫𝐞𝐩 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: load up your past campaigns in a CSV, clean it up, and get it ready for analysis. 2️⃣ 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚: analyze key features to set the right parameters for targeting. 3️⃣ 𝐒𝐮𝐩𝐞𝐫𝐥𝐢𝐧𝐤𝐞𝐝 𝐦𝐚𝐠𝐢𝐜: define your campaign schema, and explore recency, costs, and keywords to discover what works. 4️⃣ 𝐅𝐢𝐧𝐝 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐜𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬: run custom queries to unlock insights beyond basic keyword matching. But what’s really happening behind the curtains? This solution offers you multi-dimensional analysis and semantic matching for deeper insights. We’re giving this solution to you (check the link), and in return, we just ask for one thing: drop us a ⭐ on GitHub! 😊 👉 https://lnkd.in/dWa2GtAp #sharingiscaring #keyword
What if you could target your audience more precisely and improve conversion rates using smarter data insights? That's exactly what vector embeddings can do for you. In our latest article, we dive into how Superlinked’s framework empowers you to analyze user behavior based on their response to different ad creatives, using semantic vectors to extract deeper insights. So let's get to the point: do you want to know which ads attract the most active users? Click below to explore how you can take your user acquisition strategies to the next level. ⬇ https://lnkd.in/dUfvBQ3m
A new survey presents a novel taxonomy for categorizing methods that combine LLMs and Graph Neural Networks. Want a quick rundown of the key insights? Click to learn more!
Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that combine LLMs and GNNs. 🦜🌐 The authors classified methods based on how they structure the interaction between LLMs and graph learning components. This includes looking at the order of operations, how information flows between different parts of the model, and how the training and inference processes are designed. This approach identified four distinct architectural approaches ⚙️: 1️⃣ GNNs as Prefix: ▪️ GNNs first process graph data to create structure-aware tokens for LLMs to use during inference. ▪️ This approach leverages GNNs' ability to capture complex relationships, providing a solid foundation for LLMs to build upon. 2️⃣ LLMs as Prefix: ▪️ LLMs process graph data alongside textual information to generate node embeddings to improve GNN training. ▪️ This method leverages LLMs' language capabilities to improve GNN training. 3️⃣ LLMs-Graphs Integration: ▪️ This approach involves a deeper integration of LLMs and GNNs through fusion training, GNN alignment, and LLM-powered graph agents. 4️⃣ LLMs-Only: ▪️ This method converts graph-structured data into LLM-friendly sequences. Some approaches incorporate multi-modal tokens, allowing LLMs to handle graph data in innovative ways. This work goes beyond traditional taxonomies that only consider LLM's role within the system. It really digs into how these integrations work in practice 💡. The potential for LLMs to overcome the limitations of GNNs is super exciting—and we’re just starting to see what’s possible 🚀.
What a day! 🚀 It’s always inspiring to feel the energy and innovation at events like MongoDB .local in London. The Superlinked team - Ben Gutkovich, Harshil Patel, and Matt S. - brought their A-game: from podcasting and lightning talks to hackathons, panel discussions, and partner meetings, they were everywhere (you know, multi-modal vectors deserve a multi-channel presence 😉). Speaking of events, which upcoming one are you planning to attend? 🤔 Share with the community and let's meet!
What a fun (and busy) day for the Superlinked gang at MongoDB .local in London! 🙌 💪 While Harshil and Matt were schmoozing with #AIEngineers in the exhibition area, I managed to do a podcast, give an oversubscribed lightning talk on "#VectorSearch over Complex Data", participate in a panel discussion about the "Emergence of AI Engineer" and support 8 teams developing #GenAI-powered workflows to solve pain points across multiple industries as part of "AI Build Together" hackathon 😅 What did I learn? ✅ There is a lot of interest in building with GenAI models (duh!) ✅ Larger companies are starting to look at the ROI of their #AI investments ✅ Complex data (text+metadata) can't just be fed to models as is ✅ MongoDB has a large and engaged audience of AI Engineers 🛠️ A big thanks to the MongoDB team - Soumya, Jade, Shane, Harshit, Maxwell and others for organizing such a fantastic event 🙏 I'm excited about the future of #MachineLearning and the endless possibilities Vector Search opens for AI Engineers! 🚀