Retrieval Augmented Generation (RAG) emerges as a solution to bridge this gap, allowing LLMs to access external knowledge sources. This article delves into RAG, examines its elements, and constructs a usable RAG workflow that harnesses the potential of LlamaIndex, a knowledge graph.
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The IntelligentGraph capability is when intelligent agents can be embedded in an RDF graph. These agents are activated only when the graph is queried for results referencing the agent.
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Here we will explore how to scrape YouTube video transcripts into a knowledge graph for Retrieval Augmented Generation (RAG) applications. We will use Google Cloud Platform to store our initial transcripts, LangChain to create documents from the transcripts and a Neo4j graph database to store the resulting documents. In this example we will be creating a knowledge graph containing objective musical facts spoken by Anthony Fantano himself on a select few music genres.
Graph Neural Networks (GNNs) have become increasingly popular for processing graph-structured data, such as social networks, molecular graphs, and knowledge graphs. However, the complex nature of graph-based data and the non-linear relationships between nodes in a graph can make it difficult to understand why a GNN makes a particular prediction. With the rise in popularity of Graph Neural Networks, there also came an increased interest in explaining their predictions.
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2022 comes to an end and it is about time to sit down and reflect upon the achievements made in Graph ML as well as to hypothesize about possible breakthroughs in 2023.
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We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs.
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Dr. Petar Veličković is a Staff Research Scientist at DeepMind, he has firmly established himself as one of the most significant up and coming researchers in the deep learning space. He invented Graph Attention Networks in 2017 and has been a leading light in the field ever since pioneering research in Graph Neural Networks, Geometric Deep Learning and also Neural Algorithmic reasoning. If you haven’t already, you should check out our video on the Geometric Deep learning blueprint, featuring Petar.
Recommending related products — say, a phone case to go along with a new phone — is a fundamental capability of e-commerce sites, one that saves customers time and leads to more satisfying shopping experiences.
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Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse and various in the real world, it is quite challenging to design proper GNN methods for specific problems. In this tutorial, we focus on the critical challenges of GNN-based recommendation and the potential solutions.
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Today we’ll dive into the theory and implementation of the Graph Attention Network (GAT). In a nutshell: attention rocks, graphs rock, GAT’s authors rock!
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Physics-inspired continuous learning models on graphs allow to overcome the limitations of traditional GNNs
The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a…
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