SAP HANA Cloud 向量引擎

SAP HANA Cloud 向量引擎 是一个完全集成到 SAP HANA Cloud 数据库 中的向量存储。

设置

您首先需要安装 @sap/hana-client 或 hdb 包，以及 @langchain/community 包

提示

请参阅此部分，了解有关安装集成包的通用说明。

npm

npm install -S @langchain/community @langchain/core @sap/hana-client
# or
npm install -S @langchain/community @langchain/core hdb

Yarn

yarn add @langchain/community @langchain/core @sap/hana-client
# or
yarn add @langchain/community @langchain/core hdb

pnpm

pnpm add @langchain/community @langchain/core @sap/hana-client
# or
pnpm add @langchain/community @langchain/core hdb

您还需要连接到 HANA Cloud 实例的数据库。

OPENAI_API_KEY = "Your OpenAI API key"
HANA_HOST = "HANA_DB_ADDRESS"
HANA_PORT = "HANA_DB_PORT"
HANA_UID =  "HANA_DB_USER"
HANA_PWD = "HANA_DB_PASSWORD"

API 参考

从文本创建新索引

import { OpenAIEmbeddings } from "@langchain/openai";
import hanaClient from "hdb";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";

const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
  // useCesu8 : false
};
const client = hanaClient.createClient(connectionParams);
// connet 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();
    }
  });
});
const embeddings = new OpenAIEmbeddings();
const args: HanaDBArgs = {
  connection: client,
  tableName: "test_fromTexts",
};
// This function will create a table "test_fromTexts" if not exist, if exists,
// then the value will be appended to the table.
const vectorStore = await HanaDB.fromTexts(
  ["Bye bye", "Hello world", "hello nice world"],
  [
    { id: 2, name: "2" },
    { id: 1, name: "1" },
    { id: 3, name: "3" },
  ],
  embeddings,
  args
);

const response = await vectorStore.similaritySearch("hello world", 2);

console.log(response);

/* This result is based on no table "test_fromTexts" existing in the database.
  [
    { pageContent: 'Hello world', metadata: { id: 1, name: '1' } },
    { pageContent: 'hello nice world', metadata: { id: 3, name: '3' } }
  ]
*/
client.disconnect();

API 参考

• OpenAIEmbeddings 来自 @langchain/openai
• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector

从加载器创建新索引并执行相似性搜索

import hanaClient from "hdb";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { CharacterTextSplitter } from "@langchain/textsplitters";

const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
  // useCesu8 : false
};
const client = hanaClient.createClient(connectionParams);
// connet 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();
    }
  });
});
const embeddings = new OpenAIEmbeddings();
const args: HanaDBArgs = {
  connection: client,
  tableName: "test_fromDocs",
};
// Load documents from file
const loader = new TextLoader("./state_of_the_union.txt");
const rawDocuments = await loader.load();
const splitter = new CharacterTextSplitter({
  chunkSize: 500,
  chunkOverlap: 0,
});
const documents = await splitter.splitDocuments(rawDocuments);
// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.
const vectorStore = new HanaDB(embeddings, args);
await vectorStore.initialize();
// Delete already existing documents from the table
await vectorStore.delete({ filter: {} });
// add the loaded document chunks
await vectorStore.addDocuments(documents);

// similarity search (default:“Cosine Similarity”, options:["euclidean", "cosine"])
const query = "What did the president say about Ketanji Brown Jackson";
const docs = await vectorStore.similaritySearch(query, 2);
docs.forEach((doc) => {
  console.log("-".repeat(80));
  console.log(doc.pageContent);
});
/*
  --------------------------------------------------------------------------------
  One of the most serious constitutional responsibilities a President has is nominating 
  someone to serve on the United States Supreme Court. 

  And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. 
  One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
  --------------------------------------------------------------------------------
  As I said last year, especially to our younger transgender Americans, I will always have your back as your President, 
  so you can be yourself and reach your God-given potential. 

  While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. 
  From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice
*/

// similiarity search using euclidean distance method
const argsL2d: HanaDBArgs = {
  connection: client,
  tableName: "test_fromDocs",
  distanceStrategy: "euclidean",
};
const vectorStoreL2d = new HanaDB(embeddings, argsL2d);
const docsL2d = await vectorStoreL2d.similaritySearch(query, 2);
docsL2d.forEach((docsL2d) => {
  console.log("-".repeat(80));
  console.log(docsL2d.pageContent);
});

// Output should be the same as the cosine similarity search method.

// Maximal Marginal Relevance Search (MMR)
const docsMMR = await vectorStore.maxMarginalRelevanceSearch(query, {
  k: 2,
  fetchK: 20,
});
docsMMR.forEach((docsMMR) => {
  console.log("-".repeat(80));
  console.log(docsMMR.pageContent);
});
/*
  --------------------------------------------------------------------------------
  One of the most serious constitutional responsibilities a President has is nominating someone 
  to serve on the United States Supreme Court. 

  And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. 
  One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
  --------------------------------------------------------------------------------
  Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned 
  soldiers defending their homeland. 

  In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” 
  The Ukrainian Ambassador to the United States is here tonight. 

  Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world.
*/
client.disconnect();

API 参考

• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector
• OpenAIEmbeddings 来自 @langchain/openai
• TextLoader 来自 langchain/document_loaders/fs/text
• CharacterTextSplitter 来自 @langchain/textsplitters

创建 HNSW 向量索引

向量索引可以显著加快向量的 top-k 最近邻查询速度。用户可以使用 create_hnsw_index 函数创建分层可导航小世界 (HNSW) 向量索引。

有关在数据库级别创建索引的更多信息，例如参数要求，请参阅官方文档。

import hanaClient from "hdb";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";
import { OpenAIEmbeddings } from "@langchain/openai";

// table "test_fromDocs" is already created with the previous example.
// Now, we will use this existing table to create indexes and perform similarity search.

const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
};
const client = hanaClient.createClient(connectionParams);

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

// Initialize embeddings
const embeddings = new OpenAIEmbeddings();

// First instance using the existing table "test_fromDocs" (default: Cosine similarity)
const argsCosine: HanaDBArgs = {
  connection: client,
  tableName: "test_fromDocs",
};

// Second instance using the existing table "test_fromDocs" but with L2 Euclidean distance
const argsL2: HanaDBArgs = {
  connection: client,
  tableName: "test_fromDocs",
  distanceStrategy: "euclidean", // Use Euclidean distance for this instance
};

// Initialize both HanaDB instances
const vectorStoreCosine = new HanaDB(embeddings, argsCosine);
const vectorStoreL2 = new HanaDB(embeddings, argsL2);

// Create HNSW index with Cosine similarity (default)
await vectorStoreCosine.createHnswIndex({
  indexName: "hnsw_cosine_index",
  efSearch: 400,
  m: 50,
  efConstruction: 150,
});

// Create HNSW index with Euclidean (L2) distance
await vectorStoreL2.createHnswIndex({
  indexName: "hnsw_l2_index",
  efSearch: 400,
  m: 50,
  efConstruction: 150,
});

// Query text for similarity search
const query = "What did the president say about Ketanji Brown Jackson";

// Perform similarity search using the default Cosine index
const docsCosine = await vectorStoreCosine.similaritySearch(query, 2);
console.log("Cosine Similarity Results:");
docsCosine.forEach((doc) => {
  console.log("-".repeat(80));
  console.log(doc.pageContent);
});
/*
Cosine Similarity Results:
----------------------------------------------------------------------
One of the most serious constitutional ... 

And I did that 4 days ago, when I ...
----------------------------------------------------------------------
As I said last year, especially ... 

While it often appears that we never agree, that isn’t true...
*/
// Perform similarity search using Euclidean distance (L2 index)
const docsL2 = await vectorStoreL2.similaritySearch(query, 2);
console.log("Euclidean (L2) Distance Results:");
docsL2.forEach((doc) => {
  console.log("-".repeat(80));
  console.log(doc.pageContent);
});
// The L2 distance results should be the same as cosine search results.

// Disconnect from SAP HANA after the operations
client.disconnect();

API 参考

• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector
• OpenAIEmbeddings 来自 @langchain/openai

基本向量存储操作

import { OpenAIEmbeddings } from "@langchain/openai";
import hanaClient from "hdb";
// or import another node.js driver
// import hanaClient from "@sap/haha-client";
import { Document } from "@langchain/core/documents";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";

const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
  // useCesu8 : false
};
const client = hanaClient.createClient(connectionParams);
// connet 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();
    }
  });
});
const embeddings = new OpenAIEmbeddings();
// define instance args
const args: HanaDBArgs = {
  connection: client,
  tableName: "testBasics",
};

// Add documents with metadata.
const docs: Document[] = [
  {
    pageContent: "foo",
    metadata: { start: 100, end: 150, docName: "foo.txt", quality: "bad" },
  },
  {
    pageContent: "bar",
    metadata: { start: 200, end: 250, docName: "bar.txt", quality: "good" },
  },
];

// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.
const vectorStore = new HanaDB(embeddings, args);
// need to initialize once an instance is created.
await vectorStore.initialize();
// Delete already existing documents from the table
await vectorStore.delete({ filter: {} });
await vectorStore.addDocuments(docs);
// Query documents with specific metadata.
const filterMeta = { quality: "bad" };
const query = "foobar";
// With filtering on {"quality": "bad"}, only one document should be returned
const results = await vectorStore.similaritySearch(query, 1, filterMeta);
console.log(results);
/*
    [  {
        pageContent: "foo",
        metadata: { start: 100, end: 150, docName: "foo.txt", quality: "bad" }
      }
    ]
*/
// Delete documents with specific metadata.
await vectorStore.delete({ filter: filterMeta });
// Now the similarity search with the same filter will return no results
const resultsAfterFilter = await vectorStore.similaritySearch(
  query,
  1,
  filterMeta
);
console.log(resultsAfterFilter);
/*
    []
*/
client.disconnect();

API 参考

• OpenAIEmbeddings 来自 @langchain/openai
• Document 来自 @langchain/core/documents
• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector

高级过滤

除了基本的基于值的过滤功能外，还可以使用更高级的过滤。下表显示了可用的过滤器运算符。

运算符	语义
$eq	相等 (==)
$ne	不等 (!=)
$lt	小于 (<)
$lte	小于或等于 (<=)
$gt	大于 (>)
$gte	大于或等于 (>=)
$in	包含在一组给定值中 (in)
$nin	不包含在一组给定值中 (not in)
$between	介于两个边界值范围之间
$like	基于 SQL 中 "LIKE" 语义的文本相等性（使用 "%" 作为通配符）
$and	逻辑 "与"，支持 2 个或更多操作数
$or	逻辑 "或"，支持 2 个或更多操作数

import { OpenAIEmbeddings } from "@langchain/openai";
import hanaClient from "hdb";
import { Document } from "@langchain/core/documents";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";

const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
};
const client = hanaClient.createClient(connectionParams);

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

const docs: Document[] = [
  {
    pageContent: "First",
    metadata: { name: "adam", is_active: true, id: 1, height: 10.0 },
  },
  {
    pageContent: "Second",
    metadata: { name: "bob", is_active: false, id: 2, height: 5.7 },
  },
  {
    pageContent: "Third",
    metadata: { name: "jane", is_active: true, id: 3, height: 2.4 },
  },
];

// Initialize embeddings
const embeddings = new OpenAIEmbeddings();

const args: HanaDBArgs = {
  connection: client,
  tableName: "testAdvancedFilters",
};

// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.
const vectorStore = new HanaDB(embeddings, args);
// need to initialize once an instance is created.
await vectorStore.initialize();
// Delete already existing documents from the table
await vectorStore.delete({ filter: {} });
await vectorStore.addDocuments(docs);

// Helper function to print filter results
function printFilterResult(result: Document[]) {
  if (result.length === 0) {
    console.log("<empty result>");
  } else {
    result.forEach((doc) => console.log(doc.metadata));
  }
}

let advancedFilter;

// Not equal
advancedFilter = { id: { $ne: 1 } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$ne":1}}
{ name: 'bob', is_active: false, id: 2, height: 5.7 } 
{ name: 'jane', is_active: true, id: 3, height: 2.4 }
*/

// Between range
advancedFilter = { id: { $between: [1, 2] } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$between":[1,2]}}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'bob', is_active: false, id: 2, height: 5.7 } */

// In list
advancedFilter = { name: { $in: ["adam", "bob"] } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"name":{"$in":["adam","bob"]}}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'bob', is_active: false, id: 2, height: 5.7 } */

// Not in list
advancedFilter = { name: { $nin: ["adam", "bob"] } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"name":{"$nin":["adam","bob"]}}
{ name: 'jane', is_active: true, id: 3, height: 2.4 } */

// Greater than
advancedFilter = { id: { $gt: 1 } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$gt":1}}
{ name: 'bob', is_active: false, id: 2, height: 5.7 }
{ name: 'jane', is_active: true, id: 3, height: 2.4 } */

// Greater than or equal to
advancedFilter = { id: { $gte: 1 } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$gte":1}}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'bob', is_active: false, id: 2, height: 5.7 }
{ name: 'jane', is_active: true, id: 3, height: 2.4 } */

// Less than
advancedFilter = { id: { $lt: 1 } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$lt":1}}
<empty result> */

// Less than or equal to
advancedFilter = { id: { $lte: 1 } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"id":{"$lte":1}}
{ name: 'adam', is_active: true, id: 1, height: 10 } */

// Text filtering with $like
advancedFilter = { name: { $like: "a%" } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"name":{"$like":"a%"}}
{ name: 'adam', is_active: true, id: 1, height: 10 } */

advancedFilter = { name: { $like: "%a%" } };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"name":{"$like":"%a%"}}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'jane', is_active: true, id: 3, height: 2.4 } */

// Combined filtering with $or
advancedFilter = { $or: [{ id: 1 }, { name: "bob" }] };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"$or":[{"id":1},{"name":"bob"}]}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'bob', is_active: false, id: 2, height: 5.7 } */

// Combined filtering with $and
advancedFilter = { $and: [{ id: 1 }, { id: 2 }] };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"$and":[{"id":1},{"id":2}]}
<empty result> */

advancedFilter = { $or: [{ id: 1 }, { id: 2 }, { id: 3 }] };
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"$or":[{"id":1},{"id":2},{"id":3}]}
{ name: 'adam', is_active: true, id: 1, height: 10 }
{ name: 'bob', is_active: false, id: 2, height: 5.7 }
{ name: 'jane', is_active: true, id: 3, height: 2.4 } */

// You can also define a nested filter with $and and $or.
advancedFilter = {
  $and: [{ $or: [{ id: 1 }, { id: 2 }] }, { height: { $gte: 5.0 } }],
};
console.log(`Filter: ${JSON.stringify(advancedFilter)}`);
printFilterResult(
  await vectorStore.similaritySearch("just testing", 5, advancedFilter)
);
/* Filter: {"$and":[{"$or":[{"id":1},{"id":2}]},{"height":{"$gte":5.0}}]}
{ name: 'adam', is_active: true, id: 1, height: 10 } 
{ name: 'bob', is_active: false, id: 2, height: 5.7 } */

// Disconnect from SAP HANA aft er the operations
client.disconnect();

API 参考

• OpenAIEmbeddings 来自 @langchain/openai
• Document 来自 @langchain/core/documents
• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector

在链中使用 VectorStore 作为检索器进行检索增强生成 (RAG)

import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { createRetrievalChain } from "langchain/chains/retrieval";
import hanaClient from "hdb";
import {
  HanaDB,
  HanaDBArgs,
} from "@langchain/community/vectorstores/hanavector";

// Connection parameters
const connectionParams = {
  host: process.env.HANA_HOST,
  port: process.env.HANA_PORT,
  user: process.env.HANA_UID,
  password: process.env.HANA_PWD,
  // useCesu8 : false
};
const client = hanaClient.createClient(connectionParams);
// connet 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();
    }
  });
});
const embeddings = new OpenAIEmbeddings();

const args: HanaDBArgs = {
  connection: client,
  tableName: "test_fromDocs",
};
const vectorStore = new HanaDB(embeddings, args);
await vectorStore.initialize();
// Use the store as part of a chain, under the premise that "test_fromDocs" exists and contains the chunked docs.
const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo-1106" });
const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are an expert in state of the union topics. You are provided multiple context items that are related to the prompt you have to answer. Use the following pieces of context to answer the question at the end.\n\n{context}",
  ],
  ["human", "{input}"],
]);

const combineDocsChain = await createStuffDocumentsChain({
  llm: model,
  prompt: questionAnsweringPrompt,
});

const chain = await createRetrievalChain({
  retriever: vectorStore.asRetriever(),
  combineDocsChain,
});

// Ask the first question (and verify how many text chunks have been used).
const response = await chain.invoke({
  input: "What about Mexico and Guatemala?",
});

console.log("Chain response:");
console.log(response.answer);
console.log(
  `Number of used source document chunks: ${response.context.length}`
);
/*
 The United States has set up joint patrols with Mexico and Guatemala to catch more human traffickers.
 Number of used source document chunks: 4
*/
const responseOther = await chain.invoke({
  input: "What about other countries?",
});
console.log("Chain response:");
console.log(responseOther.answer);
/* Ask another question on the same conversational chain. The answer should relate to the previous answer given.
....including members of NATO, the European Union, and other allies such as Canada....
*/
client.disconnect();

API 参考

• ChatPromptTemplate 来自 @langchain/core/prompts
• ChatOpenAI 来自 @langchain/openai
• OpenAIEmbeddings 来自 @langchain/openai
• createStuffDocumentsChain 来自 langchain/chains/combine_documents
• createRetrievalChain 来自 langchain/chains/retrieval
• HanaDB 来自 @langchain/community/vectorstores/hanavector
• HanaDBArgs 来自 @langchain/community/vectorstores/hanavector

相关内容

• 向量存储概念指南
• 向量存储操作指南