使用 `HanaSparqlQAChain` 进行问答

要使用此功能，请安装 @sap/hana-langchain 包及其依赖项：

安装与配置

要使用此功能，请安装 @sap/hana-langchain 包及其依赖项：

npm install @sap/hana-langchain @langchain/core@latest @langchain/classic@latest langchain@latest

创建与您的 SAP HANA Cloud 实例的连接。

import * as dotenv from 'dotenv';
// Load environment variables if needed
dotenv.config();

import hanaClient from "@sap/hana-client";

const connectionParams = {
  host: process.env.HANA_DB_ADDRESS,
  port: process.env.HANA_DB_PORT,
  user: process.env.HANA_DB_USER,
  password: process.env.HANA_DB_PASSWORD,
};
const client = hanaClient.createConnection(connectionParams);

// connect to hanaDB
await new Promise<void>((resolve, reject) => {
  client.connect((err: Error) => {
    // Use arrow function here
    if (err) {
      reject(err);
    } else {
      console.log("Connected to SAP HANA successfully.");
      resolve();
    }
  });
});

HanaSparqlQAChain 将以下功能整合在一起：

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

初始化

• 一个 LLM 用于生成和解释查询
• 一个 HanaRdfGraph（包含连接、graph_uri 和本体）

请按照此处 HanaRdfGraph 的步骤了解如何创建一个 HanaRdfGraph 实例。

导入 HanaSparqlQAChain

import { HanaSparqlQAChain } from "@sap/hana-langchain";
const qaChain = HanaSparqlQAChain.fromLLM({ llm, graph, allowDangerousRequests: true, verbose: true });

管道概述

1. SPARQL 生成
   • 使用 SPARQL 生成提示词
   • 输入：schema（来自 graph.getSchema() 的 Turtle 格式本体），prompt（用户问题）
2. 查询后处理
   • 从模型输出中提取 SPARQL
   • 如果缺少 FROM <graph_uri>，则注入
   • 确保常用前缀（rdf:、rdfs:、owl:、xsd:）
3. 执行
   • graph.query(generatedSparql)
4. 答案生成
   • 使用包含输入 context（结果）、prompt（原始问题）的 QA 提示词

提示词模板

”SPARQL 生成” 提示词

sparqlGenerationPrompt 用于指导 LLM 根据用户问题和提供的模式生成 SPARQL 查询。

答案生成提示词

qaPrompt 指示 LLM 仅根据数据库结果创建自然语言答案。 默认提示词可在以下位置找到：prompts.ts

自定义提示词

您可以在初始化时覆盖默认值：

const qaChain = HanaSparqlQAChain.fromLLM({
  llm,
  graph,
  allowDangerousRequests: true,
  verbose: true,
  sparqlGenerationPrompt: YOUR_SPARQL_PROMPT,
  qaPrompt: 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：在“电影”知识图谱上进行问答

前提条件： 您必须有一个启用了 三元组存储 功能的 SAP HANA Cloud 实例。 有关详细说明，请参阅：启用三元组存储
加载 kgdocu_movies 示例数据。参见 知识图谱示例。

下面我们将：

1. 实例化指向我们“电影”数据图谱的 HanaRdfGraph
2. 使用 LLM 将其包装在 HanaSparqlQAChain 中
3. 提出自然语言问题并打印出链的响应

这展示了 LLM 如何在底层生成 SPARQL、执行它针对 SAP HANA 并返回人类可读的答案。

首先，创建与您的 SAP HANA Cloud 实例的连接。

import * as dotenv from 'dotenv';
// Load environment variables if needed
dotenv.config();

import hanaClient from "@sap/hana-client";

const connectionParams = {
  host: process.env.HANA_DB_ADDRESS,
  port: process.env.HANA_DB_PORT,
  user: process.env.HANA_DB_USER,
  password: process.env.HANA_DB_PASSWORD,
};
const client = hanaClient.createConnection(connectionParams);

// connect to hanaDB
await new Promise<void>((resolve, reject) => {
  client.connect((err: Error) => {
    // Use arrow function here
    if (err) {
      reject(err);
    } else {
      console.log("Connected to SAP HANA successfully.");
      resolve();
    }
  });
});

然后，设置知识图谱实例

import { HanaRdfGraph, HanaRdfGraphOptions } from "@sap/hana-langchain";

const graphOptions: HanaRdfGraphOptions = {
  connection: client,
  graphUri: "kgdocu_movies",
  autoExtractOntology: true,
};

// create a Graph instance from a source URI
const graph = new HanaRdfGraph(graphOptions);

// need to initialize once an instance is created.
await graph.initialize(graphOptions);

// Serialise the graph schema (optional)
// Internally, the schema is stored as an N3 Store instance,
// We use the N3 Writer to serialise it to Turtle format for display.
const schemaStore = graph.getSchema();
const writer = new Writer({
  prefixes: {
    rdf: "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    rdfs: "http://www.w3.org/2000/01/rdf-schema#",
    owl: "http://www.w3.org/2002/07/owl#",
    xsd: "http://www.w3.org/2001/XMLSchema#",
  },
});
schemaStore.forEach((quad) => {
  writer.addQuad(quad);
});
writer.end((error, result) => {
  if (error) {
    console.error("Error serialising schema:", error);
  } else {
    console.log("Graph Schema in Turtle format:\n", result);
  }
});

Graph Schema in Turtle format:
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
@prefix owl: <http://www.w3.org/2002/07/owl#>.
@prefix xsd: <http://www.w3.org/2001/XMLSchema#>.

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

接下来，初始化 LLM。

import { ChatOpenAI } from "@langchain/openai"; // or your chosen LLM
const llm = new ChatOpenAI({ model: "gpt-4o" });

然后，我们创建一个 SPARQL QA 链

import { HanaSparqlQAChain, HanaSparqlQAChainOptions } from "@sap/hana-langchain";

const chainOptions: HanaSparqlQAChainOptions = {
  llm,
  allowDangerousRequests: true,
  graph,
  debug: true,
};

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

const response = await chain.invoke({ query });
console.log(response["result"]);

Generated SPARQL:
\`\`\`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 "Blade Runner" .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
}
\`\`\`
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 "Blade Runner" .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
}


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/Q723780,Brion James
http://www.wikidata.org/entity/Q81328,Q81328
http://www.wikidata.org/entity/Q498420,M. Emmet Walsh
http://www.wikidata.org/entity/Q1691628,Joe Turkel
http://www.wikidata.org/entity/Q207596,Daryl Hannah
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/Q230736,Sean Young
http://www.wikidata.org/entity/Q211415,Edward James Olmos

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

底层工作原理

1. SPARQL 生成 链通过 SPARQL_GENERATION_SELECT_PROMPT 调用 LLM，使用 Turtle 格式的本体（graph.getSchema()）和用户问题。LLM 然后生成一个针对您的模式的合法 SELECT 查询。
2. 预处理与执行
   • 提取和清理：从 LLM 的响应中提取原始 SPARQL 文本。
   • 注入图上下文：如果缺少 FROM <graph_uri>，则添加并确保声明常用前缀（rdf:、rdfs:、owl:、xsd:）。
   • 在 HANA 上执行：通过 HanaRdfGraph.query() 在您的命名图中执行最终查询。
3. 答案生成 返回的 CSV（或 Turtle）结果再次输入 LLM—这次使用 SPARQL_QA_PROMPT。LLM 生成简洁、人类可读的答案，严格基于检索到的数据，不会产生幻觉。

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官方原文：src/oss/javascript/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。