This repository is a full-stack application for building knowledge graphs from unstructured data using Large Language Models (LLMs). The backend, built with FastAPI and LangChain, ingests documents from multiple sources (local files, web pages, YouTube, Wikipedia, S3, GCS), chunks them, and uses an LLM to extract entities and relationships, which are stored in a Neo4j graph database. The frontend, a React/TypeScript application, provides a user interface for uploading files, configuring extraction (schema, LLM model, chunking), visualizing the resulting graph, and interacting with the data through a retrieval-augmented generation (RAG) chatbot with multiple retrieval modes (vector, graph, fulltext, hybrid). The system includes post-processing features like community detection, deduplication, and orphan node cleanup, as well as token usage tracking and evaluation metrics (RAGAS). While primarily an application with a web UI, it also contains experimental notebooks (GraphRAG, chunk size evaluation) that serve a data research purpose, making it a hybrid codebase with a dominant application character.