Azure Cosmos DB for MongoDB vCore（已弃用）

使用 LangChain JavaScript 集成 Azure Cosmos DB for MongoDB vCore 向量存储。

此集成已重命名。 Azure Cosmos DB for MongoDB vCore 已被微软重新命名为 Azure DocumentDB。

请使用新的 Azure DocumentDB 集成。旧的类名（AzureCosmosDBMongoDBVectorStore 等）仍可用作向后兼容的弃用别名，但你应该迁移到新的 AzureDocumentDBVectorStore 类。

Azure Cosmos DB for MongoDB vCore 可轻松创建一个具有完整原生 MongoDB 支持的数据库。您可以将您的 MongoDB 经验应用于应用程序，并通过指向 MongoDB vCore 账户的连接字符串继续使用您喜欢的 MongoDB 驱动程序、SDK 和工具。在 Azure Cosmos DB for MongoDB vCore 中使用向量搜索，可无缝地将基于 AI 的应用程序与存储在 Azure Cosmos DB 中的数据集成。

Azure Cosmos DB for MongoDB vCore 为开发者提供了一种完全托管的 MongoDB 兼容数据库服务，用于构建具有熟悉架构的现代应用程序。

从 此页面 了解如何利用 Azure Cosmos DB for MongoDB vCore 的向量搜索功能。如果您没有 Azure 账户，可以通过 创建免费账户 开始使用。

设置

您首先需要安装 @langchain/azure-cosmosdb 包：

参见 此部分关于安装 LangChain 包的一般说明。

npm

npm install @langchain/azure-cosmosdb @langchain/core

您还需要运行一个 Azure Cosmos DB for MongoDB vCore 实例。您可以在 Azure 门户上按照 此指南 部署免费版本，无需任何费用。

在您运行实例后，请确保您有连接字符串和管理密钥。您可以在 Azure 门户中，在您的实例的“连接字符串”部分找到它们。然后您需要设置以下环境变量：

.env vars

AZURE_COSMOSDB_MONGODB_CONNECTION_STRING=

示例

下面是一个示例，它从 Azure Cosmos DB for MongoDB vCore 中的文件索引文档，运行向量搜索查询，并最终使用链基于检索到的文档用自然语言回答一个问题。

import {
  AzureCosmosDBMongoDBVectorStore,
  AzureCosmosDBMongoDBSimilarityType,
} from "@langchain/azure-cosmosdb";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { createStuffDocumentsChain } from "@langchain/classic/chains/combine_documents";
import { createRetrievalChain } from "@langchain/classic/chains/retrieval";
import { TextLoader } from "@langchain/classic/document_loaders/fs/text";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

// Load documents from file
const loader = new TextLoader("./state_of_the_union.txt");
const rawDocuments = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 0,
});
const documents = await splitter.splitDocuments(rawDocuments);

// Create Azure Cosmos DB for MongoDB vCore vector store
const store = await AzureCosmosDBMongoDBVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings(),
  {
    databaseName: "langchain",
    collectionName: "documents",
    indexOptions: {
      numLists: 100,
      dimensions: 1536,
      similarity: AzureCosmosDBMongoDBSimilarityType.COS,
    },
  }
);

// Performs a similarity search
const resultDocuments = await store.similaritySearch(
  "What did the president say about Ketanji Brown Jackson?"
);

console.log("Similarity search results:");
console.log(resultDocuments[0].pageContent);
/*
  Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

  Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

  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.
*/

// Use the store as part of a chain
const model = new ChatOpenAI({ model: "gpt-3.5-turbo-1106" });
const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "Answer the user's questions based on the below context:\n\n{context}",
  ],
  ["human", "{input}"],
]);

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

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

const res = await chain.invoke({
  input: "What is the president's top priority regarding prices?",
});

console.log("Chain response:");
console.log(res.answer);
/*
  The president's top priority is getting prices under control.
*/

// Clean up
await store.delete();

await store.close();

相关

• 向量存储 概念指南
• 向量存储 操作指南

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