以编程方式使用文档

设置与安装

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

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

创建到 SAP HANA Cloud 实例的连接。

// Load environment variables if needed
dotenv.config();


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 将以下内容整合在一起：

  1. **模式感知的 SPARQL 生成**
  2. **查询执行** 针对 SAP HANA
  3. **自然语言答案格式化**

初始化

  • * An **LLM** 生成和解释查询
  • * A **HanaRdfGraph** （使用连接、 graph_uri和本体）

请按照以下步骤操作 HanaRdfGraph 了解更多关于创建 HanaRdfGraph instance.

导入 HanaSparqlQAChain

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 - Inputs: schema （来自 Turtle graph.getSchema()), prompt （用户问题） 2. 查询后处理 - 从模型输出中提取 SPARQL - 注入 FROM <graph_uri> 如果缺失 - 确保常用前缀（rdf:, rdfs:, owl:, xsd:) 3. 执行 - graph.query(generatedSparql) 4. 答案组织 - 使用包含以下输入的 QA 提示： context （结果）、 prompt （原始问题）

提示模板

“SPARQL 生成”提示

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

问答提示

qaPrompt 指导 LLM 仅根据数据库结果创建自然语言答案。 默认提示可在此处找到： prompts.ts

自定义提示

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 必须具有以下输入变量： ["schema", "prompt"] > - qa_prompt 必须具有以下输入变量： ["context", "prompt"]

示例：基于“电影”知识图的问答

前提条件: 您必须拥有已启用 **三元组存储** 功能的 SAP HANA Cloud 实例。 详细说明请参阅： 启用三元组存储<br /> 加载 kgdocu_movies 示例数据。请参阅 知识图示例.

下面我们将：

  1. 实例化 HanaRdfGraph 指向我们的“movies”数据图
  2. 将其包装在 HanaSparqlQAChain 由LLM驱动
  3. 提出自然语言问题并打印出链的响应

这演示了LLM如何在底层生成SPARQL，对SAP HANA执行它，并返回人类可读的答案。

首先，创建到SAP HANA Cloud实例的连接。

// Load environment variables if needed
dotenv.config();


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，设置知识图实例

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>.

之后，初始化LLM。

const llm = new ChatOpenAI({ model: "gpt-4o" });

然后，我们创建一个SPARQL问答链

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.

底层发生了什么？

1. **SPARQL生成** 该链使用您的Turtle格式本体（graph.getSchema()）和用户的问题，使用 SPARQL_GENERATION_SELECT_PROMPT。然后LLM发出一个有效的 SELECT 针对您的模式定制的查询。 2. **预处理与执行** * **提取与清洗**：从LLM的响应中提取原始SPARQL文本。 * **注入图上下文**：添加 FROM <graph_uri> 如果缺失则添加，并确保声明常见前缀（rdf:, rdfs:, owl:, xsd:）。 * **在HANA上运行**：通过以下方式执行最终查询 HanaRdfGraph.query() 对您的命名图执行。 3. **答案构建** 返回的CSV（或Turtle）结果再次输入LLM——这次附带 SPARQL_QA_PROMPT。LLM根据检索到的数据生成简洁、易读的答案，不会产生幻觉。