Refactoring the iText2KG code

-The entire iText2KG code has been refactored by adding data models that describe an Entity, a Relationship, and a Knowledge Graph.

• Each entity is embedded using both its name and label to avoid merging concepts with similar names but different labels, such as Python: Language and Python: Snake.
• The weights for entity name embedding and entity label are configurable, with defaults set to 0.4 for the entity label and 0.6 for the entity name.
• A max_tries parameter has been added to the iText2KG.build_graph function for entity and relation extraction to prevent hallucinatory effects in structuring the output. A max_tries_isolated_entities parameter has been added to the same method to handle hallucinatory effects when processing isolated entities.

Latest update

• Fixing the bug reported in #7.
• Update the iText2KG build_graph function to perform matching between newly constructed graphs and existing graphs after the construction process.

Supporting other LLMs

• Now, iText2KG is compatible with all the chat/embeddings models LangChain supports. (#1)
  The constructed graph can be expanded by passing the already extracted entities and relationships as arguments to the build_graph function in iText2KG.
• iText2KG is compatible with all Python versions above 3.9. (#2)
• Some bugs in the overall architecture are corrected.

Latest update

• Updating the version of neo4j in the requirements.
• Adding datasets for threshold estimation.
• Adding the paper link.

Fixing the workflow

V0.0.2

Update dependencies in publish_on_pypi.yml and setup.cfg

First Release

V0.0.1

Add PyPI publishing workflow and configuration files