SAP HANA Cloud Knowledge Graph Engine

SAP HANA Cloud Knowledge Graph is a fully integrated knowledge graph solution within the SAP HANA Cloud database.

Setup & Installation

You must have an SAP HANA Cloud instance with the triple store feature enabled. For detailed instructions, refer to: Enable Triple Store To use SAP HANA Knowledge Graph Engine with LangChain, install the langchain-hana package:

pip install langchain_hana

First, create a connection to your SAP HANA Cloud instance.

import os

from dotenv import load_dotenv
from hdbcli import dbapi

# Load environment variables if needed
load_dotenv()

# Establish connection to SAP HANA Cloud
connection = dbapi.connect(
    address=os.environ.get("HANA_DB_ADDRESS"),
    port=os.environ.get("HANA_DB_PORT"),
    user=os.environ.get("HANA_DB_USER"),
    password=os.environ.get("HANA_DB_PASSWORD")
)

Then, import the HanaRdfGraph Class

from langchain_hana import HanaRdfGraph

Creating a `HanaRdfGraph` instance

The constructor requires:

connection: an active hdbcli.dbapi.connect(...) instance
graph_uri: the named graph (or "DEFAULT") where your RDF data lives
One of:
1. ontology_query: a SPARQL CONSTRUCT to extract schema triples
2. ontology_uri: a hosted ontology graph URI
3. ontology_local_file + ontology_local_file_format: a local Turtle/RDF file
4. auto_extract_ontology=True (not recommended for production—see note)

graph_uri vs. Ontology

graph_uri: The named graph in your SAP HANA Cloud instance that contains your instance data (sometimes 100k+ triples). If None, "" or "DEFAULT" is provided, the default graph is used.
Ontology: a lean schema (typically ~50-100 triples) describing classes, properties, domains, ranges, labels, comments, and subclass relationships. The ontology guides SPARQL generation and result interpretation.

Creating a graph instance with DEFAULT graph

More info on the DEFAULT graph can be found at DEFAULT Graph and Named Graphs.

graph = HanaRdfGraph(
    connection=connection,
    auto_extract_ontology=True,
)

# graph = HanaRdfGraph(
#     connection=connection,
#     graph_uri="DEFAULT",
#     auto_extract_ontology=True,
# )

# graph = HanaRdfGraph(
#     connection=connection,
#     graph_uri="",
#     auto_extract_ontology=True,
# )

Creating a graph instance with a `graph_uri`

graph = HanaRdfGraph(
    connection=connection,
    graph_uri="http://example.org/movies",
    auto_extract_ontology=True,
)

Creating a graph instance with a remote `ontology_uri`

Load the schema directly from a hosted graph URI.

graph = HanaRdfGraph(
    connection=connection,
    ontology_uri="<your_ontology_graph_uri>",
)

Creating a graph instance with a custom `ontology_query`

Use a custom CONSTRUCT query to selectively extract schema triples.

ontology_query = """
    PREFIX owl: <http://www.w3.org/2002/07/owl#>
    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
    PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
    CONSTRUCT {?cls rdf:type owl:Class . ?cls rdfs:label ?clsLabel . ?rel rdf:type ?propertyType . ?rel rdfs:label ?relLabel . ?rel rdfs:domain ?domain . ?rel rdfs:range ?range .}
    FROM <kgdocu_movies>
    WHERE { # get properties
        {SELECT DISTINCT ?domain ?rel ?relLabel ?propertyType ?range
        WHERE {
            ?subj ?rel ?obj .
            ?subj a ?domain .
            OPTIONAL{?obj a ?rangeClass .}
            FILTER(?rel != rdf:type)
            BIND(IF(isIRI(?obj) = true, owl:ObjectProperty, owl:DatatypeProperty) AS ?propertyType)
            BIND(COALESCE(?rangeClass, DATATYPE(?obj)) AS ?range)
            BIND(STR(?rel) AS ?uriStr)       # Convert URI to string
            BIND(REPLACE(?uriStr, "^.*[/#]", "") AS ?relLabel)
        }}
        UNION { # get classes
            SELECT DISTINCT ?cls ?clsLabel
            WHERE {
                ?instance a/rdfs:subClassOf* ?cls .
                FILTER (isIRI(?cls)) .
                BIND(STR(?cls) AS ?uriStr)       # Convert URI to string
                BIND(REPLACE(?uriStr, "^.*[/#]", "") AS ?clsLabel)
            }
        }
    }
"""

# can provide the graph_uri param as well if needed
graph = HanaRdfGraph(
    connection=connection,
    ontology_query=ontology_query,
)

Creating a graph instance with a Local rdf file

(ontology_local_file + ontology_local_file_format): Load the schema from a local RDF ontology file. Supported RDF formats are Turtle, RDF/XML, JSON-LD, N-Triples, Notation-3, Trig, Trix, N-Quads.

graph = HanaRdfGraph(
    connection=connection,
    ontology_local_file="<your_ontology_file_path>", # e.g., "ontology.ttl"
    ontology_local_file_format="<your_ontology_file_format>",  # e.g., "Turtle", "RDF/XML", "JSON-LD", "N-Triples", "Notation-3", "Trig", "Trix", "N-Quads"
)

Auto extraction of ontology

(auto_extract_ontology=True): Infer schema information directly from your instance data.

graph = HanaRdfGraph(
    connection=connection,
    graph_uri="<your_graph_uri>",
    auto_extract_ontology=True,
)

Note: Auto-extraction is not recommended for production—it omits important triples like rdfs:label, rdfs:comment, and rdfs:subClassOf in general.

Executing SPARQL queries

You can use the query() method to execute arbitrary SPARQL queries (SELECT, ASK, CONSTRUCT, etc.) on the data graph. The function has the following parameters

query: the SPARQL query string.
content_type: the response format for the output (Default is CSV)

Please use the following strings for the respective formats.

CSV: "sparql-results+xml"
JSON: "sparql-results+json"
XML: "sparql-results+csv"
TSV: "sparql-results+tsv"

Note: CONSTRUCT and ASK Queries return turtle and boolean formats respectively.

Let us insert some data into the Puppets graph.

cursor = connection.cursor()
try:
    result = cursor.callproc(
        "SYS.SPARQL_EXECUTE", (
        """
        INSERT DATA {
        GRAPH <Puppets> {
            <P1> a <Puppet>; <name> "Ernie"; <show> "Sesame Street".
            <P2> a <Puppet>; <name> "Bert"; <show> "Sesame Street" .
            }
        }
        """, "", "?", "?"
        )
    )
    response = result[2]
except dbapi.Error as db_error:
    raise RuntimeError(
        f'The database query failed: '
        f'{db_error.errortext.split("; Server Connection")[0]}'
    )
finally:
    cursor.close()

Then, we create a graph instance for the Puppets graph.

puppets_graph = HanaRdfGraph(
    connection=connection,
    graph_uri="Puppets",
    auto_extract_ontology=True,
)

The given query lists all tuples in the Puppets graph.

query = """
SELECT ?s ?p ?o
WHERE {
    GRAPH <Puppets> {
        ?s ?p ?o .
    }
}
ORDER BY ?s
"""

result = puppets_graph.query(query)
print(result)

s,p,o
P1,name,Ernie
P1,show,Sesame Street
P1,http://www.w3.org/1999/02/22-rdf-syntax-ns#type,Puppet
P2,name,Bert
P2,show,Sesame Street
P2,http://www.w3.org/1999/02/22-rdf-syntax-ns#type,Puppet

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SAP HANA Cloud Knowledge Graph Engine

Setup & Installation

Creating a `HanaRdfGraph` instance

Creating a graph instance with DEFAULT graph

Creating a graph instance with a `graph_uri`

Creating a graph instance with a remote `ontology_uri`

Creating a graph instance with a custom `ontology_query`

Creating a graph instance with a Local rdf file

Auto extraction of ontology

Executing SPARQL queries

​Setup & Installation

​Creating a HanaRdfGraph instance

​Creating a graph instance with DEFAULT graph

​Creating a graph instance with a graph_uri

​Creating a graph instance with a remote ontology_uri

​Creating a graph instance with a custom ontology_query

​Creating a graph instance with a Local rdf file

​Auto extraction of ontology

​Executing SPARQL queries

Setup & Installation

Creating a `HanaRdfGraph` instance

Creating a graph instance with DEFAULT graph

Creating a graph instance with a `graph_uri`

Creating a graph instance with a remote `ontology_uri`

Creating a graph instance with a custom `ontology_query`

Creating a graph instance with a Local rdf file

Auto extraction of ontology

Executing SPARQL queries