> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Question Answering with HanaSparqlQAAgent

> Integrate with the HanaSparqlQAAgent type using LangChain Python.

## Setup and Installation

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

1. **Dynamically retrieve the ontology** using a dedicated tool
2. **Generate and execute SPARQL queries** iteratively
3. **Self-correct** if a query fails or returns unexpected results
4. **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](/oss/python/integrations/graphs/sap_hana_rdf_graph) to know more about creating a `HanaRdfGraph` instance.

Import the `HanaSparqlQAAgent`

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_hana import HanaSparqlQAAgent
agent = HanaSparqlQAAgent.create_agent(graph=graph, model=llm)
```

## Agent Overview

The agent uses tools iteratively to:

1. First retrieve the ontology to understand the data structure
2. Generate appropriate SPARQL queries based on the schema
3. Execute queries and interpret results
4. 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`:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-knowledge-graph-guide/enable-triple-store/)<br />
Load the `kgdocu_movies` example data. See [Knowledge Graph Example](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-knowledge-graph-guide/knowledge-graph-example).

The example below:

1. Instantiates the `HanaRdfGraph` pointing at the movies data graph
2. Creates a `HanaSparqlQAAgent` powered by an LLM
3. 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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from gen_ai_hub.proxy.langchain.openai import ChatOpenAI
from langchain_hana import HanaRdfGraph, HanaSparqlQAChain

# from langchain_openai import ChatOpenAI  # or your chosen LLM
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Set up the Knowledge Graph
graph_uri = "kgdocu_movies"

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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"))
```

```output theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
@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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Initialize the LLM
llm = ChatOpenAI(proxy_model_name="gpt-4o", temperature=0)
```

Then, create the SPARQL QA Agent.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Create a SPARQL QA Agent
agent = HanaSparqlQAAgent.create_agent(
    graph=graph,
    model=llm,
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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()
```

```output theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}

================================ 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?

1. **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.

2. **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.

3. **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.

4. **Answer Formulation**
   The agent interprets the query results and formulates a concise, human-readable answer based strictly on the retrieved data.

***

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