ConceptGraphBuilder 0.1.1

A Python library for extracting concepts using LLMs and building knowledge graphs

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ConceptGraphBuilder

ConceptGraphBuilder is a Python library designed for extracting concepts from unstructured text using Large Language Models (LLMs) and constructing knowledge graphs that visualize the relationships between these concepts. This library provides an intuitive interface for processing text data, extracting meaningful concepts, building graphs, detecting communities, and visualizing the results in an interactive format.

Features

• Document Loading and Processing: Seamlessly load documents from directories and split them into manageable chunks.
• Concept Extraction: Leverage the power of LLMs to extract key concepts and relationships directly from the text.
• Knowledge Graph Construction: Automatically build knowledge graphs that represent the extracted concepts and their relationships.
• Community Detection: Identify and visualize communities within the graph using advanced network analysis algorithms.
• Interactive Visualization: Visualize the graph in an interactive HTML format, making it easy to explore complex relationships.

Installation

You can install the ConceptGraphBuilder library via pip:

pip install ConceptGraphBuilder

Quick Start Guide

1. Load and Process Documents

First, load your documents and split them into manageable chunks:

from concept_graph_builder import DataLoader

# Initialize the DataLoader with the directory containing your documents
loader = DataLoader("path/to/data")

# Load documents from the directory
documents = loader.load_documents()

# Split documents into smaller chunks
pages = loader.split_documents(documents)

# Convert the document chunks into a Pandas DataFrame
df = loader.documents_to_dataframe(pages)

2. Extract Concepts and Relationships Using LLM

Next, extract the key concepts and relationships from the text using an LLM:

from concept_graph_builder import LLMExtractor

# Initialize the LLMExtractor
extractor = LLMExtractor()

# Extract concepts and relationships from the first document chunk
concepts = extractor.graph_prompt(df["text"].iloc[0])

# Optionally, you can extract general concepts from the text
general_concepts = extractor.extract_concepts(df["text"].iloc[0])

3. Build and Analyze the Knowledge Graph

Now, use the extracted concepts to build a knowledge graph and analyze it:

from concept_graph_builder import GraphBuilder

# Initialize the GraphBuilder
builder = GraphBuilder()

# Build the graph from the DataFrame
graph = builder.build_graph(df)

# Detect communities within the graph
communities = builder.detect_communities()

4. Visualize the Knowledge Graph

Finally, visualize the graph and explore the relationships interactively:

from concept_graph_builder import GraphVisualizer

# Initialize the GraphVisualizer
visualizer = GraphVisualizer(graph)

# Assign colors to the nodes based on their community
colors_df = visualizer.assign_colors(communities)

# Add node attributes like color and size
visualizer.add_node_attributes(colors_df)

# Visualize the graph and save it as an HTML file
visualizer.visualize_graph("output.html")

Usage Scenarios

• Text Analysis and NLP: Use ConceptGraphBuilder to analyze large text datasets, extract meaningful relationships, and visualize them as knowledge graphs.
• Research and Academia: Useful for researchers and academics working on text mining, data visualization, and knowledge management.
• Business Intelligence: Companies can leverage this tool to analyze customer feedback, support tickets, or any unstructured text data to uncover hidden insights.

Documentation

For more detailed documentation, including API references and advanced usage examples, please visit our documentation page. (Replace # with the actual link to your documentation)

Contributing

We welcome contributions to ConceptGraphBuilder! If you would like to contribute, please fork the repository, make your changes, and submit a pull request. Be sure to review our contribution guidelines before you start. (Replace # with the actual link to your contribution guidelines)

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions, issues, or suggestions, feel free to open an issue on GitHub or contact us directly at [e-mail].

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