Add graphrag-neo4j module: structured-output entity and relation extraction into a typed Neo4j graph with a vector index, multi-hop retrieval by span linking and variable-length Cypher paths, compared with plain vector RAG over the same 35 chunks and 9 questions (complete evidence 7/9 vs 5/9 at k=8); Testcontainers on neo4j:2026.10.1 and 5.26. The extraction step is a rule-based stand-in, not a language model.

Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01CifR2BE7PBvRy5hT8TS9oz

graphrag-neo4j/output/07-fixed-shape-cypher.txt

@@ -0,0 +1,23 @@
+# Questions whose shape is known: the Cypher is the retrieval (hand-written, parameterised, run on the real graph)
+
+
+--- Q7  Which services stop working if the Orders Database goes down? ---
+MATCH (db:Entity {name: $name})<-[:DEPENDS_ON*1..5]-(s:Entity)
+RETURN DISTINCT s.name AS service ORDER BY service
+  -> Checkout Service
+  -> Ledger Service
+  -> Payments Gateway
+
+--- Q6  Who leads the teams that own the services the Checkout Service depends on? ---
+MATCH (c:Entity {name: $name})-[:DEPENDS_ON]->(d:Entity)-[:OWNED_BY]->(team:Entity)-[:LED_BY]->(lead:Entity)
+RETURN d.name AS dependency, team.name AS team, lead.name AS lead ORDER BY dependency
+  -> Inventory Service / Fulfilment Team / Tomasz Nowak
+  -> Payments Gateway / Payments Team / Priya Raman
+
+--- Q8  entity linking and the traversal in ONE statement: the vector index finds the seed, MATCH walks from it ---
+CALL db.index.vector.queryNodes('entity_name_embedding', 1, $vector) YIELD node AS c, score
+MATCH (c)-[:DEPENDS_ON]->(d:Entity)<-[:AFFECTED]-(i:Entity)
+RETURN c.name AS seed, score, d.name AS dependency, i.name AS incident ORDER BY incident
+  $vector = embedding of the span "Checkout Service"
+  -> seed Checkout Service (1.00): INC-2057 affected Inventory Service
+  -> seed Checkout Service (1.00): INC-2060 affected Payments Gateway