sap-hana-triple

name	sap-hana-triple
description	Use this skill when building Knowledge Graphs on SAP HANA Cloud's Triple Store — SPARQL 1.1 modeling, graph workspaces, ingestion from S/4 or business data, and the agent-over-KG NL→SPARQL→answer pattern. Includes hybrid KG+vector retrieval where both engines live in the same HANA tenant.

SAP HANA Cloud Triple Store — SPARQL 1.1 / Knowledge Graphs

HANA Cloud's Triple Store is SPARQL 1.1 over a graph workspace. You model your domain as RDF triples (subject, predicate, object), load them, and query with SPARQL.

Graph workspace

CREATE GRAPH IF NOT EXISTS COE_PILOT WORKSPACE;

Modeling — ontology in Turtle

@prefix : <http://coe.example/customer#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

:Customer a rdfs:Class .
:hasPrimaryContact a rdf:Property ;
    rdfs:domain :Customer ; rdfs:range :Contact .

Keep the ontology in ontology/*.ttl files in the project — code-review changes the same way you do schema changes.

Before loading: data preparation

Run sap-hana-data-prep before this skill. It produces the entity mapping and Turtle triples this skill consumes:

prepared/entity_mapping.yaml      -- entity classes, id fields, relationship predicates, IRI base
prepared/triples.ttl              -- sample or full RDF in Turtle
prepared/ingestion-contract.yaml  -- confirms target_shape: hana_triple, pii_handling

If prepared/ingestion-contract.yaml is absent or target_shape is not hana_triple, return to sap-hana-data-prep.

Loading

Bulk load via SPARQL INSERT DATA over an hdbcli connection:

import hdbcli.dbapi as db
conn = db.connect(address=HANA_HOST, port=443, user=USER, password=PASS, encrypt=True)
cur = conn.cursor()
cur.execute("""
SPARQL INSERT DATA {
  GRAPH <coe-pilot> {
    <http://coe.example/customer#acme> a <http://coe.example/customer#Customer> ;
        <http://coe.example/customer#hasPrimaryContact> <http://coe.example/customer#alice> .
    <http://coe.example/customer#alice> a <http://coe.example/customer#Contact> .
  }
}""")

Query

cur.execute("""
SPARQL SELECT ?contactLabel WHERE {
  GRAPH <coe-pilot> {
    <http://coe.example/customer#acme> <http://coe.example/customer#hasPrimaryContact> ?c .
    ?c <http://www.w3.org/2000/01/rdf-schema#label> ?contactLabel .
  }
}""")

Agent-over-KG pattern

1. Few-shot the LLM with the ontology to emit SPARQL from NL.
2. Execute the SPARQL.
3. Re-prompt the LLM to phrase the result.

This pattern lives conceptually in references/kg-project/agent/kg-agent.py (planned template; tracked as TODO in references/README.md). Until it lands, the SPARQL-over-HANA walkthrough is in recipes/optional/hana-kg-triple-store/.

Hybrid KG + Vector

You can put REAL_VECTOR columns on a graph's annotation table and use a vector pre-filter to narrow candidate nodes before SPARQL traversal. Not yet documented as its own recipe — combine recipes/optional/hana-vector-store/ and recipes/optional/hana-kg-triple-store/ for now.

Pitfalls

• Forgetting the GRAPH <name> clause → query hits the default graph and returns nothing.
• IRI vs literal confusion: enclose IRIs in < >, literals in " ".
• Mixing tenants: each HANA Cloud tenant has its own workspace; cross-tenant SPARQL is not transparent.
• Insert performance: batch INSERT DATA in chunks of 1000 triples — much faster than one-by-one.

Verify

cur.execute("SPARQL SELECT (COUNT(*) AS ?n) WHERE { GRAPH <coe-pilot> { ?s ?p ?o } }")
n = cur.fetchone()[0]
assert n > 0, "graph is empty — re-run ingest"

Cross-references

• Upstream data prep: skills/sap-hana-data-prep/ — run this first for any new dataset
• Walkthrough: recipes/optional/hana-kg-triple-store/
• Template: references/kg-project/ — planned; tracked as TODO in references/README.md.
• HANA docs: https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-sparql-1-1-reference