Graph-based NLP with GraphRAG in Python: An Overview

Graph-based NLP with GraphRAG in Python: An Overview

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In this video, we will provide an overview of GraphRAG, a graph-based library for Natural Language Processing (NLP) tasks using Python. GraphRAG utilizes graph neural networks to model text data, offering superior performance and interpretability compared to traditional sequence models.

The library is particularly beneficial for applications like text classification, information extraction, and graphs-based tasks. To begin, let's briefly discuss graph neural networks (GNNs) and how they operate on text data.

Eventually, we'll walk you through setting up a development environment and work through examples to better understand GraphRAG's features and capabilities. For further study, we recommend exploring papers such as:

1. Graph Convolutional Networks by Thomas N. Kipf and Max Welling (2016)br
2. Graph Attention Networks by Vaswani et al. (2017)br
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Devlin et al. (2018)br


Additional Resources:
1. Official Documentation: https://graphrag.readthedocs.io/en/la...
2. GitHub Repository: https://github.com/dlk-ai/GraphRAG

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