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Create knowledge graphs with LLMs

Project description

llmgraph

llmgraph enables you to create knowledge graphs in GraphML, GEXF, and HTML formats from a given source entity Wikipedia page. The knowledge graphs are generated by extracting world knowledge from ChatGPT or other large language models (LLMs).

Features

  • Create knowledge graphs from a source entity Wikipedia page.
  • Support for generating knowledge graphs in HTML, GraphML, and GEXF formats.
  • Utilizes the power of ChatGPT and other large language models to extract world knowledge.

Installation

You can install llmgraph using pip:

pip install llmgraph

Example Output

In addition to GraphML and GEXF formats, an HTML pyvis physics enabled graph can be viewed:

example machine learning output

Example Usage

The example above was generated with the following command:

llmgraph machine-learning "https://en.wikipedia.org/wiki/Artificial_intelligence" --levels 3

It used a total of 7,650 gpt-3.5-turbo tokens to render 3 layers from the root 'Artificial Intelligence' node.

Required Arguments

  • entity_type (TEXT): Entity type (e.g. movie)
  • entity_wikipedia (TEXT): Full Wikipedia link to the root entity

Optional Arguments

  • --entity-root (TEXT): Optional root entity name override if different from the Wikipedia page title [default: None]
  • --levels (INTEGER): Number of levels deep to construct from the central root entity [default: 2]
  • --max-sum-total-tokens (INTEGER): Maximum sum of tokens for graph generation [default: 200000]
  • --output-folder (TEXT): Folder location to write outputs [default: ./_output/]
  • --llm-model (TEXT): The model name [default: gpt-3.5-turbo]
  • --llm-temp (FLOAT): LLM temperature value [default: 0.0]
  • --llm-use-localhost (INTEGER): LLM use localhost:8081 instead of OpenAI [default: 0]
  • --help: Show this message and exit.

Contributing

We welcome contributions to llmgraph. To contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Make your changes and commit them.
  4. Create a pull request with a clear description of your changes.

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