This is a full-stack application (FastAPI backend, React frontend) that builds knowledge graphs from unstructured data using LLMs. It ingests documents from local files, S3, GCS, web pages, YouTube, and Wikipedia, chunks them, extracts entities and relationships via LangChain's LLMGraphTransformer, and stores the graph in Neo4j. The system provides a chatbot interface with multiple retrieval modes (vector, graph, hybrid, fulltext, community search), post-processing capabilities (community detection, duplicate merging, vector indexes), and RAGAS-based evaluation metrics. The architecture is a monolithic backend with a single FastAPI app and a React SPA frontend, orchestrated via Docker Compose. External dependencies include Neo4j, multiple LLM providers (OpenAI, Gemini, Anthropic, etc.), and optional cloud storage integrations. The codebase is actively maintained with recent commits and issue triage, and shows low technical drift despite some aspirational model names and minor duplication.