spec · graphrag-hierarchical-chat · prototype · 2026
GraphRAG Hierarchical Chat
Graph-structured retrieval over long, cross-referenced document sets
- Role
- Sole engineer
- Domain
- RAG / Retrieval
- Year
- 2026
- Status
- prototype
stack →PythonLangGraphNeo4j / graph storeEmbeddingsFastAPI
no interface capture on record
Source ↗A retrieval layer that builds a hierarchy of entity and summary nodes over a corpus, so a query can walk from broad context down to specific passages instead of relying on flat top-k similarity.
01 · Problem
Flat vector search collapses on long documents that cross-reference each other: the chunks that answer a question are often not the chunks most similar to it, and top-k retrieval has no notion of 'zoom level'.
02 · Approach
- 01Extract entities and relationships per chunk, then cluster into a multi-level graph — leaf chunks, mid-level summaries, corpus-level themes.
- 02Route a query first against summary nodes to pick a subgraph, then expand along edges to gather the leaf chunks that actually ground the answer.
- 03Validate every generated claim against its cited node before returning; unsupported spans are dropped.
- 04Provider-agnostic interfaces for the embedding model and graph store so the pipeline is not tied to one vendor.
03 · Outcome
- Answers on multi-hop questions stay grounded where flat RAG hallucinated the connective tissue.
- Retrieval traces are inspectable — you can see which node the model walked to and why.