llmgraph 0.3.0

LLM created knowledge graphs

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 Usage

llmgraph concepts-general "https://en.wikipedia.org/wiki/Many-worlds_interpretation" --levels 2

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.

Example Output

TODO: llmgraph concepts-general "https://en.wikipedia.org/wiki/Knowledge_graph" --levels 3
TODO: llmgraph company ??? TODO: llmgraph software-engineering TODO: llmgraph machine-learning TODO: llmgraph movie

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.