Alex KAMENI

Alex KAMENI

Machine Learning Engineer | Data Scientist | AI Researcher

GraphRAG-Tagger

less than 1 minute read

Published:

GraphRAG-Tagger is an end-to-end lightweight toolkit for extracting topics from PDFs and visualizing their connections using graphs.

Why Use GraphRAG-Tagger?

If you’re transitioning from traditional RAG (Retrieval-Augmented Generation) to GraphRAG, defining interactions between chunks is crucial. GraphRAG-Tagger automates this process by:

  • Extracting topics from PDFs.
  • Constructing topic similarity graphs.
  • Making retrieval more structured and context-aware.

Key Features

  • Automates topic extraction from PDFs.
  • Visualizes topic connections using graph structures.
  • Facilitates the transition from RAG to GraphRAG.
  • Enhances retrieval workflows with structured and context-aware interactions.

Skills and Technologies

  • Retrieval-Augmented Generation (RAG): Supports advanced retrieval workflows.
  • GraphRAG: Enables structured and graph-based retrieval.
  • CI/CD: Designed with continuous integration and delivery in mind.

GraphRAG-Tagger is an essential toolkit for developers and researchers looking to enhance their retrieval systems by leveraging graph-based approaches. Simplify your transition to GraphRAG today!

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