This project demonstrates how to create and visualize a knowledge graph by processing textual data from PDF documents. The knowledge graph is stored in a Neo4j database and evolves dynamically as new data is ingested. The system adheres to software engineering best practices, ensuring a scalable, maintainable, and robust implementation.

Features

Dynamic Knowledge Graph Construction:
    Ingests batches of PDFs to extract meaningful relationships.
    Constructs and updates a Neo4j knowledge graph.
Batch Processing:
    Processes 5 PDFs initially, with additional PDFs added incrementally.
    Ensures the graph evolves without redundant reprocessing.
Visualization:
    Visualizes the graph in Neo4j's UI.
Error Handling:
    Robust error management during PDF ingestion, graph updates, and database interactions.
Scalability:
    Designed for seamless integration with larger datasets and workflows.