使用 `HanaSparqlQAChain` 进行问答

要使用此功能，安装langchain-hana包：

安装与配置

要使用此功能，安装langchain-hana包：

pip install langchain_hana

然后，创建与您的SAP HANA云实例的连接。

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")
)

HanaSparqlQAChain整合了以下功能：

1. 基于模式的SPARQL生成
2. 对SAP HANA的查询执行
3. 自然语言答案格式化

初始化

需要：

• 一个用于生成和解析查询的 LLM
• 一个包含连接、graph_uri和本体论的 HanaRdfGraph

按照HanaRdfGraph的步骤获取创建HanaRdfGraph实例的详细信息。

导入HanaSparqlQAChain

from langchain_hana import HanaSparqlQAChain

qa_chain = HanaSparqlQAChain.from_llm(
    llm=llm, graph=graph, allow_dangerous_requests=True, verbose=True
)

流程概述

1. SPARQL生成
   • 使用SPARQL_GENERATION_SELECT_PROMPT
   • 输入：
     • schema（来自graph.get_schema的Turtle格式）
     • prompt（用户的问题）
2. 查询后处理
   • 从LLM响应中提取SPARQL代码
   • 若缺失则注入FROM <graph_uri>
   • 确保声明必需的公共前缀（rdf:、rdfs:、owl:、xsd:）
3. 执行
   • 调用graph.query(generated_sparql)
4. 答案生成
   • 使用SPARQL_QA_PROMPT
   • 输入：
     • context（原始查询结果）
     • prompt（原始问题）

提示词模板

”SPARQL生成”提示词

sparql_generation_prompt用于指导LLM根据用户问题及提供的模式生成SPARQL查询。

答案生成提示词

qa_prompt要求LLM仅基于数据库结果生成自然语言回答。

默认提示词可在此找到：prompts.py

自定义提示词

初始化时可覆盖默认值：

qa_chain = HanaSparqlQAChain.from_llm(
    llm=llm,
    graph=graph,
    allow_dangerous_requests=True,
    verbose=True,
    sparql_generation_prompt=YOUR_SPARQL_PROMPT,
    qa_prompt=YOUR_QA_PROMPT
)

• sparql_generation_prompt必须包含输入变量：["schema", "prompt"]
• qa_prompt必须包含输入变量：["context", "prompt"]

示例：基于“电影”知识图谱的问答

前提条件： 需在SAP HANA云实例中启用三元组存储功能。 详细配置步骤请参考：启用三元组存储
加载kgdocu_movies示例数据。详见Knowledge Graph示例。

具体操作步骤：

1. 初始化指向“电影”数据图谱的HanaRdfGraph
2. 用LLM构建HanaSparqlQAChain
3. 提问并输出链式响应

本示例演示LLM如何生成SPARQL查询、执行SAP HANA查询并返回可读答案。

首先创建SAP HANA云实例连接。

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")
)

接着配置知识图谱实例

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" .

然后初始化LLM。

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

创建SPARQL问答链

# Create a SPARQL QA Chain
chain = HanaSparqlQAChain.from_llm(
    llm=llm,
    verbose=True,
    allow_dangerous_requests=True,
    graph=graph,
)

# output = chain.invoke("Which movies are in the data?")
# output = chain.invoke("In which movies did Keanu Reeves and Carrie-Anne Moss play in together")
# output = chain.invoke("which movie genres are in the data?")
# output = chain.invoke("which are the two most assigned movie genres?")
# output = chain.invoke("where were the actors of "Blade Runner" born?")
# output = chain.invoke("which actors acted together in a movie and were born in the same city?")
output = chain.invoke("which actors acted in Blade Runner?")

print(output["result"])

> Entering new HanaSparqlQAChain chain...
Generated SPARQL:
\`\`\`
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 ?actor ?actorLabel
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title ?movieTitle .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
    FILTER(?movieTitle = "Blade Runner")
}
\`\`\`
Final SPARQL:

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 ?actor ?actorLabel

FROM <kgdocu_movies>
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title ?movieTitle .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
    FILTER(?movieTitle = "Blade Runner")
}

Full Context:
actor,actorLabel
http://www.wikidata.org/entity/Q1353691,Morgan Paull
http://www.wikidata.org/entity/Q1372770,William Sanderson
http://www.wikidata.org/entity/Q358990,James Hong
http://www.wikidata.org/entity/Q498420,M. Emmet Walsh
http://www.wikidata.org/entity/Q81328,Q81328
http://www.wikidata.org/entity/Q723780,Brion James
http://www.wikidata.org/entity/Q207596,Daryl Hannah
http://www.wikidata.org/entity/Q1691628,Joe Turkel
http://www.wikidata.org/entity/Q236702,Joanna Cassidy
http://www.wikidata.org/entity/Q213574,Rutger Hauer
http://www.wikidata.org/entity/Q3143555,Hy Pyke
http://www.wikidata.org/entity/Q211415,Edward James Olmos
http://www.wikidata.org/entity/Q230736,Sean Young


> Finished chain.
The actors who acted in Blade Runner are Morgan Paull, William Sanderson, James Hong, M. Emmet Walsh, Brion James, Daryl Hannah, Joe Turkel, Joanna Cassidy, Rutger Hauer, Hy Pyke, Edward James Olmos, and Sean Young.

内部机制解析

1. SPARQL生成 链调用LLM，使用SPARQL_GENERATION_SELECT_PROMPT处理格式化本体（graph.get_schema）和用户问题，生成适配模式的合法SELECT查询。
2. 预处理与执行
   • 提取与清理：从LLM响应中提取原始SPARQL文本
   • 注入图上下文：若缺失则添加FROM <graph_uri>，确保声明公共前缀（rdf:、rdfs:、owl:、xsd:）
   • 执行：通过HanaRdfGraph.query()在命名图中运行优化后的查询
3. 答案生成 返回的CSV（或Turtle）结果通过SPARQL_QA_PROMPT再次输入LLM，生成严格基于数据且无虚构的简洁回答。

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官方原文：src/oss/python/integrations/chains/sap_hana_sparql_qa_chain.mdx · 非官方翻译，译文由 AI 生成，以英文原文为准。