Project / 02
GraphRAGAgent
A knowledge exploration application combining a graph, vector retrieval, D3 visualization, and multi-turn question answering.
The problem to solve
Knowledge questions need to expose both retrieved context and the relationships between pieces of knowledge. This project combines GraphRAG, vector retrieval, multi-turn questions, and graph visualization in one application structure.
My design and implementation
I organized the application around a REST API, frontend pages, and a D3 graph view so that the question flow and relationship view can be used together. The README reports 25 REST endpoints and 5 frontend pages; those figures are retained as repository evidence.
System architecture
The application is composed of a REST API, retrieval and question-answering paths, frontend pages, and a D3 graph view. Vector retrieval supports relevant-content recall while the graph path exposes entity relationships; this page does not infer runtime results beyond the README evidence.
Key technical decisions
GraphRAG and vector retrieval are combined to retain semantic recall and relationship exploration. D3 expresses the graph in the frontend, while the REST API provides the boundary between the interface and the retrieval/question paths.
Results and validation evidence
The README reports 25 REST endpoints, 5 frontend pages, a D3 graph, and 44 integration tests. This section repeats repository evidence only; it adds no performance metric or external acceptance claim.
Known limitations and next steps
The validation scope is bounded by the README and repository tests, rather than a claim about production scale or real-user outcomes. A next step is to add more specific validation with real material while keeping the existing API and graph-view boundaries explicit.