Ever wondered how Spotify nails your music recommendations? Or how Pinterest seems to read your mind? It's all thanks to vector embeddings! We've taken our in-depth article on personalized search and distilled it into a bite-sized video. Perfect for busy professionals who want to stay ahead of the curve in AI and machine learning. In just one minute, you'll learn: - What vector embeddings are and why they're revolutionary - How companies like Spotify are using them to boost user engagement - The basics of implementing vector search in your own projects 🔗 Check out the video here: https://buff.ly/4cmIhv2 📚 For a deeper dive, read the comprehensive article: https://buff.ly/4cddxfM #VectorEmbeddings #AISearch #MachineLearning #DataScience #PersonalizedSearch #RecommendationSystems #Superlinked
Superlinked
Data Infrastructure and Analytics
San Francisco, California 3,167 followers
The data engineer’s solution to turning data into vector embeddings.
About us
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.
- Website
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https://superlinked.com/
External link for Superlinked
- Industry
- Data Infrastructure and Analytics
- Company size
- 11-50 employees
- Headquarters
- San Francisco, California
- Type
- Privately Held
- Founded
- 2021
- Specialties
- Personalization, Developer APIs, Cloud Infrastructure, Information Retrieval, and Vector Embedding Compute
Locations
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Primary
Get directions
166 Geary St
San Francisco, California 94108, US
Employees at Superlinked
Updates
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We are thrilled to announce that Superlinked is partnering with Redis to help enterprise tech teams build smarter and faster #GenAI apps🤝 Speed is a competitive advantage in business and you can now deploy Superlinked Server in production and integrate with the lightning-fast Redis Vector Search to build real-time, vector-powered solutions, such as Recommender Systems, Personalised Search, Fraud Detection and more 🚀 Kudos to the members of the Redis and Superlinked teams who made this valuable integration and partnership a reality! Blair Pierson, Tyler Hutcherson, Jim Allen Wallace, Balázs Kemenes, Mór Kapronczay, Edvard Grosz, György Móra and many others 🙌🙌🙌 Read the full announcement with a step-by-step guide 👉 https://lnkd.in/erngxz-n #datascience #realtime #recsys #semanticsearch
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We've just released a new video that summarizes our in-depth article on Representation Learning on Graph Structured Data in just 1 minute! 🎥⏰ In this video, we cover: ✅ The limitations of traditional Bag-of-Words approaches for node representation ✅ The power of Node2Vec for static graphs and GraphSAGE for dynamic graphs ✅ How combining LLM node features with Node2Vec or GraphSAGE can boost your node classification results ✅ Pro tips for tuning parameters, controlling inference time, and balancing embedding influence 🔗 Check out the video here: https://buff.ly/4dcwS1T 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3SC9KS8 #GraphML #NodeEmbeddings #MachineLearning #DataScience #Node2Vec #GraphSAGE #LLMs
Beyond Bag-of-Words: Harnessing Node2Vec, GraphSAGE, and LLMs for Enhanced Node Embeddings
https://www.youtube.com/
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We've just released a new video that summarizes our article on Improving RAG performance with Knowledge Graphs. 🤖💡 🔍 In this quick, informative video, we highlight the key insights from the article, including: The limitations of standard language models and RAG How knowledge graphs enable advanced reasoning capabilities 5 pro tips for combining knowledge graphs and embeddings effectively 🔗 Check out the video here: https://buff.ly/3yiiVjZ 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3yiiVR1 #LanguageAI #KnowledgeGraphs #Embeddings #ArtificialIntelligence #MachineLearning #TechTips #Superlinked
Knowledge Graphs & Embeddings: The Ultimate Language AI Combo for Unbeatable Results 🏆
https://www.youtube.com/
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Introducing our latest video: A summary of the fascinating article on Knowledge Graph Embeddings (KGEs). ⏰ In just a few minutes, you'll learn how KGEs can help you in filling in missing links, uncovering hidden connections and capturing semantic meaning from relational data. We dive into the power of KGEs, discuss the DistMult algorithm, and share insights and tips. 🔗 Check out the video here: https://buff.ly/46yCktA 📚 For a deeper dive, read the comprehensive article: https://buff.ly/3ywcuK3 #KnowledgeGraphEmbeddings #DataScience #MachineLearning #Superlinked
How Knowledge Graph Embeddings (KGEs) Uncover Hidden Connections in Relational Data
https://www.youtube.com/
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#RAG insights! 🤩
One Year of RAG: What we've learned. Some of the top minds in the industry shared their reflections after one year of intense RAG implementation and research. This summary distills the most actionable takeaways. Here's the scoop: 1️⃣ RAG's Power Triple. RAG's output depends on three factors: 🔝 Relevance. • Measures how well retrieved info matches the query. • Quantified by Mean Reciprocal Rank (MRR) or Normalized Discounted Cumulative Gain (NDCG). • Ranking of retrieved items significantly impacts downstream task performance–just as in recommendation systems. ◾ Information Density • Represents the ratio of useful information to filler content. • High-density sources provide more value per word, streamlining the RAG process. • Example: In movie reviews, top-rated and editorial reviews tend to be information-rich. 🔬 Level of Detail. • Reflects the specificity of relevant information. • High detail enables more precise and contextually aware responses. Irrelevant, sparse, or vague documents lead to poor results, while highly relevant, dense, and detailed ones enable more accurate and insightful generation. 2️⃣ Don't Overlook Keyword Search. While embedding-based retrieval has dominated RAG discussions, traditional keyword search (BM25) is still crucial. Strengths of keyword search: • Computationally efficient. • Familiar and interpretable. • Excels at queries involving names, acronyms and IDs. The optimal approach is often hybrid: • Keyword matching for exact matches. • Embeddings for synonyms, hypernyms, spelling errors, and multimodal content. The key is to leverage both methods, adjusting each to fit the specific use cases. 3️⃣ RAG Triumphs Over Fine Tuning. Recent studies consistently show RAG outperforming both unsupervised and supervised fine-tuning across general and specialized domains, for both familiar and novel information. More key RAG advantages: • Easier and cheaper knowledge base updates. • Granular access control, simplifying multi-client information management. 4️⃣ 10M Tokens Aren't Enough. Despite advancements in long-context models, RAG remains key: • Large context processing doesn't guarantee effective reasoning across all data. • Using long-context models to their full extent for every query would be financially unsound. • Without proper retrieval and ranking, we risk overwhelming the model with irrelevant information. • Transformer processing time scales linearly with context length, making full-context analysis for every query impractical. Kudos to Eugene Yan, Bryan Bischof, Charles Frye, Hamel H., Jason Liu and Shreya Shankar for uncovering and sharing freely these sought-after insights.
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Superlinked reposted this
Knowledge Graph Embeddings (KGEs) for Q&A Tasks... Beyond Words. 🤯💡 In our tests, KGEs outperformed LLMs by 10x while using 3x smaller embeddings. Here's how: Missing edges in KG lead to biased recommendations and inaccurate answers. By design, LLMs, struggle with reasoning over relational structures. 📉 Enter KGE - the missing piece in your AI toolkit! 🧰 KGEs can: 🔮 Predict missing edges in incomplete KG. 🕸️ Infer relationships that are not explicitly stated. 🧠 Interpret context and extract semantic relationships. In this tutorial, Richard Kiss demonstrates the power of DIstMult KGE for Q&A tasks. 🎯 10x higher hit rates than LLMs. ✅ Produce correct 1st answers more often than LLMs' 10 attempts. 🥇 Strong performance despite 3x smaller embeddings (250D vs 768D). So, don't let relational complexity hold back your projects! Leverage KGEs to unlock new levels of relational understanding! Learn how to implement KGEs step-by-step now 👇👇
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⭐⭐⭐Yay! Our GitHub repo hit 360 stars⭐⭐⭐ We thought that this is a number worth celebrating, as 360 degrees represent a complete view, similar to how Superlinked allows developers to achieve a complete view of ALL their data in #GenAI applications. 🔎 Want to turn ALL your structured and unstructured data into custom embeddings? Check out: https://lnkd.in/gwh8j5qp 💪
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Get your #RAG PoC off the ground! 🚀 Sign up now for the learning (and a chance to win a pair of AirPods Pro)! 🏆
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Are you (also) building a RAG-based chatbot? 🤖 Don't make beginners' mistakes 😎 And don't miss this webinar 🎯 Join us for an interactive webinar, led by Mór, Superlinked's Lead ML Engineer, to learn how to build high-quality RAG solutions that properly understand enterprise data 📑 📑 📑 👇👇👇 https://lnkd.in/eeQyCYmA Now with an opportunity to win AirPods Pro (even if you cannot attend) and guaranteed entry to the sold-out Data Science Festival's Oktoberfest event 😍 [Image credit: ChatGPT 4o - that's what happens when you let your LLMs run amok😉] #datascience #rag #machinelearning #ai #genai
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