graphrag-tagger 0.1.1

A lightweight toolkit for extracting topics from PDFs and visualizing their connections using graphs.

graphrag-tagger

A lightweight toolkit for extracting topics from PDFs and visualizing their connections using graphs.

Overview

graphrag-tagger automates topic extraction from PDF documents and builds graphs to visualize relationships between text segments. It offers a modular pipeline for processing text, applying topic modeling, refining results with an LLM, and constructing a graph-based representation of topic similarities.

Key Features

✅ PDF Processing – Extracts text from PDFs efficiently.
✅ Text Segmentation – Splits extracted text into manageable chunks.
✅ Topic Modeling – Supports two methods:

• Scikit-learn: Classic Latent Dirichlet Allocation (LDA) for topic extraction.
• ktrain: A deep-learning-based approach with vocabulary filtering.
  ✅ LLM-Powered Refinement – Uses a language model to clean and classify topics.
  ✅ Graph Construction – Builds topic similarity graphs using network analysis.

Core Dependencies

• PyMuPDF – Extracts text from PDF files.
• scikit-learn & ktrain – Performs topic modeling.
• LLM Client – Enhances and refines extracted topics.
• networkx – Constructs and analyzes graphs.

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Installation

Ensure you have Python installed, then build and install the package locally:

python -m build
pip install .

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Usage

Extract Topics from PDFs

Run the topic extraction pipeline on a folder of PDFs:

python -m graphrag_tagger.tagger \
    --pdf_folder /path/to/pdfs \
    --output_folder /path/to/output \
    --chunk_size 512 \
    --chunk_overlap 25 \
    --n_features 512 \
    --min_df 2 \
    --max_df 0.95 \
    --llm_model ollama:phi4 \
    --model_choice sk

Build a Topic Similarity Graph

Generate a graph from the extracted topics:

python -m graphrag_tagger.build_graph \
    --input_folder /path/to/output \
    --output_folder /path/to/graph \
    --threshold_percentile 97.5

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How It Works

1️⃣ PDF Processing – Extracts raw text from documents.
2️⃣ Text Segmentation – Divides the text into structured chunks.
3️⃣ Topic Modeling – Uses either LDA or ktrain-based modeling to extract key topics.
4️⃣ LLM-Based Refinement – Cleans and classifies topics for better accuracy.
5️⃣ Graph Construction – Builds a network where:

• Nodes represent text chunks.
• Edges represent topic similarities.
• The graph reveals clusters and connections between document sections.

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Contributing

Contributions are welcome! Feel free to submit issues or pull requests.