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