Question Answering with HanaSparqlQAAgent

Setup and Installation

To use this feature, install the langchain-hana package:

pip install langchain_hana

HanaSparqlQAAgent is an agent-based approach for answering questions over RDF data stored in SAP HANA Cloud. Unlike the chain-based approach, the agent can:

Dynamically retrieve the ontology using a dedicated tool
Generate and execute SPARQL queries iteratively
Self-correct if a query fails or returns unexpected results
Reason step-by-step about complex questions

Initialization

You need:

An LLM to power the agent’s reasoning
A HanaRdfGraph (with connection, graph_uri, and ontology)

Follow the steps here HanaRdfGraph to know more about creating a HanaRdfGraph instance. Import the HanaSparqlQAAgent

from langchain_hana import HanaSparqlQAAgent
agent = HanaSparqlQAAgent.create_agent(graph=graph, model=llm)

Agent Overview

The agent uses tools iteratively to:

First retrieve the ontology to understand the data structure
Generate appropriate SPARQL queries based on the schema
Execute queries and interpret results
Formulate natural language answers

Defaults

Tools:
- retrieve_ontology - Retrieves the RDF ontology/schema from the graph in Turtle format
- execute_sparql - Executes SPARQL queries against the HANA RDF graph
System Prompt: A default system prompt is provided with instructions for SPARQL generation and tool usage.
Middleware: ModelRetryMiddleware(max_retries=3) and ToolRetryMiddleWare(max_retries=2) is added by default to prevent infinite loops

Customizing Your Agent

You can customize the agent’s behavior by providing additional parameters to create_agent:

agent = HanaSparqlQAAgent.create_agent(
    graph=graph,  # Required: HanaRdfGraph instance
    model=llm,  # Required: LLM to power the agent
    system_prompt=your_custom_prompt,  # Custom system prompt (str or SystemMessage)
    tools=[your_custom_tools],  # Additional tools to include
    middleware=[your_custom_middlewares],  # Additional middleware to include
    include_default_tools=True,  # include default tools (default: True)
    include_default_middleware=True,  # include default middleware (default: True)
)

Example: Question answering over a “Movies” knowledge graph

Prerequisite: You must have an SAP HANA Cloud instance with the triple store feature enabled. For detailed instructions, refer to: Enable Triple Store
Load the kgdocu_movies example data. See Knowledge Graph Example. The example below:

Instantiates the HanaRdfGraph pointing at the movies data graph
Creates a HanaSparqlQAAgent powered by an LLM
Asks natural-language questions and lets the agent reason through the answers

This demonstrates how the agent dynamically retrieves the ontology, generates SPARQL queries, and returns human-readable answers.

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, set up the knowledge graph instance

from gen_ai_hub.proxy.langchain.openai import ChatOpenAI
from langchain_hana import HanaRdfGraph, HanaSparqlQAChain

# from langchain_openai import ChatOpenAI  # or your chosen LLM

# Set up the Knowledge Graph
graph_uri = "kgdocu_movies"

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

# a basic graph schema is extracted from the data graph. This schema will guide the LLM to generate a proper SPARQL query.
schema_graph = graph.get_schema
print(schema_graph.serialize(format="turtle"))

@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://kg.demo.sap.com/acted_in> a owl:ObjectProperty ;
    rdfs:label "acted_in" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/dateOfBirth> a owl:DatatypeProperty ;
    rdfs:label "dateOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range xsd:dateTime .

<http://kg.demo.sap.com/directed> a owl:ObjectProperty ;
    rdfs:label "directed" ;
    rdfs:domain <http://kg.demo.sap.com/Director> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/genre> a owl:ObjectProperty ;
    rdfs:label "genre" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range <http://kg.demo.sap.com/Genre> .

<http://kg.demo.sap.com/placeOfBirth> a owl:ObjectProperty ;
    rdfs:label "placeOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Place> .

<http://kg.demo.sap.com/title> a owl:DatatypeProperty ;
    rdfs:label "title" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range xsd:string .

rdfs:label a owl:DatatypeProperty ;
    rdfs:label "label" ;
    rdfs:domain <http://kg.demo.sap.com/Actor>,
        <http://kg.demo.sap.com/Director>,
        <http://kg.demo.sap.com/Genre>,
        <http://kg.demo.sap.com/Place> ;
    rdfs:range xsd:string .

<http://kg.demo.sap.com/Director> a owl:Class ;
    rdfs:label "Director" .

<http://kg.demo.sap.com/Genre> a owl:Class ;
    rdfs:label "Genre" .

<http://kg.demo.sap.com/Place> a owl:Class ;
    rdfs:label "Place" .

<http://kg.demo.sap.com/Actor> a owl:Class ;
    rdfs:label "Actor" .

<http://kg.demo.sap.com/Film> a owl:Class ;
    rdfs:label "Film" .

After that, initialise the LLM.

# Initialize the LLM
llm = ChatOpenAI(proxy_model_name="gpt-4o", temperature=0)

Then, create the SPARQL QA Agent.

# Create a SPARQL QA Agent
agent = HanaSparqlQAAgent.create_agent(
    graph=graph,
    model=llm,
)

# Example queries:
# question = "Which movies are in the data?"
# question = "In which movies did Keanu Reeves and Carrie-Anne Moss play together"
# question = "which movie genres are in the data?"
# question = "which are the two most assigned movie genres?"
# question = "where were the actors of 'Blade Runner' born?"
# question = "which actors acted together in a movie and were born in the same city?"
question = "which actors acted in Blade Runner?"

for event in agent.stream(
    {"messages": [{"role": "user", "content": question}]},
    stream_mode="values",
):
    event["messages"][-1].pretty_print()

================================ Human Message =================================

which actors acted in Blade Runner?
================================== Ai Message ==================================
Tool Calls:
  retrieve_ontology (call_9T3Dxy3zKczTQKVAgTxPBfQm)
 Call ID: call_9T3Dxy3zKczTQKVAgTxPBfQm
  Args:
================================== Ai Message ==================================
Tool Calls:
  retrieve_ontology (call_9T3Dxy3zKczTQKVAgTxPBfQm)
 Call ID: call_9T3Dxy3zKczTQKVAgTxPBfQm
  Args:
================================= Tool Message =================================
Name: retrieve_ontology

Ontology Information:
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://kg.demo.sap.com/acted_in> a owl:ObjectProperty ;
    rdfs:label "acted_in" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/dateOfBirth> a owl:DatatypeProperty ;
    rdfs:label "dateOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range xsd:dateTime .

<http://kg.demo.sap.com/directed> a owl:ObjectProperty ;
    rdfs:label "directed" ;
    rdfs:domain <http://kg.demo.sap.com/Director> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/genre> a owl:ObjectProperty ;
    rdfs:label "genre" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range <http://kg.demo.sap.com/Genre> .

<http://kg.demo.sap.com/placeOfBirth> a owl:ObjectProperty ;
    rdfs:label "placeOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Place> .

<http://kg.demo.sap.com/title> a owl:DatatypeProperty ;
    rdfs:label "title" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range xsd:string .

rdfs:label a owl:DatatypeProperty ;
    rdfs:label "label" ;
    rdfs:domain <http://kg.demo.sap.com/Actor>,
        <http://kg.demo.sap.com/Director>,
        <http://kg.demo.sap.com/Genre>,
        <http://kg.demo.sap.com/Place> ;
    rdfs:range xsd:string .

<http://kg.demo.sap.com/Director> a owl:Class ;
    rdfs:label "Director" .

<http://kg.demo.sap.com/Genre> a owl:Class ;
    rdfs:label "Genre" .

<http://kg.demo.sap.com/Place> a owl:Class ;
    rdfs:label "Place" .

<http://kg.demo.sap.com/Actor> a owl:Class ;
    rdfs:label "Actor" .

<http://kg.demo.sap.com/Film> a owl:Class ;
    rdfs:label "Film" .


================================== Ai Message ==================================
Tool Calls:
  execute_sparql (call_LwHuDXaPsx3nDJF7Hn6If8s9)
 Call ID: call_LwHuDXaPsx3nDJF7Hn6If8s9
  Args:
    query: PREFIX kg: <http://kg.demo.sap.com/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?actorLabel
FROM <kgdocu_movies>
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title "Blade Runner" .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
}
================================== Ai Message ==================================
Tool Calls:
  execute_sparql (call_LwHuDXaPsx3nDJF7Hn6If8s9)
 Call ID: call_LwHuDXaPsx3nDJF7Hn6If8s9
  Args:
    query: PREFIX kg: <http://kg.demo.sap.com/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?actorLabel
FROM <kgdocu_movies>
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title "Blade Runner" .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
}
================================= Tool Message =================================
Name: execute_sparql

SPARQL Query Result:
actorLabel
Morgan Paull
William Sanderson
James Hong
M. Emmet Walsh
Joe Turkel
Brion James
Q81328
Daryl Hannah
Rutger Hauer
Joanna Cassidy
Hy Pyke
Sean Young
Edward James Olmos

================================== Ai Message ==================================

The actors who acted in "Blade Runner" are:

- Morgan Paull
- William Sanderson
- James Hong
- M. Emmet Walsh
- Joe Turkel
- Brion James
- Daryl Hannah
- Rutger Hauer
- Joanna Cassidy
- Hy Pyke
- Sean Young
- Edward James Olmos

================================== Ai Message ==================================

The actors who acted in "Blade Runner" are:

- Morgan Paull
- William Sanderson
- James Hong
- M. Emmet Walsh
- Joe Turkel
- Brion James
- Daryl Hannah
- Rutger Hauer
- Joanna Cassidy
- Hy Pyke
- Sean Young
- Edward James Olmos

What’s happening under the hood?

Ontology Retrieval The agent first uses the retrieve_ontology tool to fetch the RDF schema in Turtle format. This gives it understanding of the available classes, properties, and relationships.
SPARQL Generation Based on the ontology and the user’s question, the agent reasons about which entities and properties to query, then generates a valid SELECT query.
Query Execution The agent calls the execute_sparql tool to run the generated query against the HANA RDF graph. If the query fails or returns unexpected results, the agent can self-correct and try again.
Answer Formulation The agent interprets the query results and formulates a concise, human-readable answer based strictly on the retrieved data.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.

Popular Providers

Integrations by component

Question Answering with HanaSparqlQAAgent

Setup and Installation

Initialization

Agent Overview

Defaults

Customizing Your Agent

Example: Question answering over a “Movies” knowledge graph

What’s happening under the hood?

​Setup and Installation

​Initialization

​Agent Overview

​Defaults

​Customizing Your Agent

​Example: Question answering over a “Movies” knowledge graph

​What’s happening under the hood?

Setup and Installation

Initialization

Agent Overview

Defaults

Customizing Your Agent

Example: Question answering over a “Movies” knowledge graph

What’s happening under the hood?