Jira Graph RAG
A local-first GraphRAG system that combines semantic retrieval with Jira issue-link graph expansion for evidence-backed triage answers.
What it is
Jira Graph RAG is a learning-focused project built to understand GraphRAG end-to-end. It ingests a public Jira dataset, normalizes issues and link relationships, then combines FAISS-based semantic search with Neo4j graph expansion to produce hybrid-ranked answers with traceable citations. The system demonstrates how semantic retrieval alone misses relational context, and how graph neighbor expansion improves coverage on relation-heavy queries.
Why I built it
I built this project to deeply learn GraphRAG architectures by implementing the full pipeline from scratch. The goal was to understand hybrid retrieval tradeoffs, evaluate evidence quality with real metrics, and build practical debugging workflows for RAG systems.
What it does
- 01End-to-end GraphRAG pipeline from raw Jira data to citation-first answers
- 02Hybrid retrieval fusing FAISS semantic seeds with Neo4j graph neighbors
- 03Evaluation framework with Recall@10 and MRR metrics
- 04Streamlit app with Answer, Evidence, and Graph Debug views
- 05Configurable embedding backends (local TF-IDF or Gemini)