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Overview

Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making. With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results. This will help you getting started with the Amazon Kendra retriever. For detailed documentation of all AWSKendraRetriever features and configurations head to the API reference.

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Integration details

RetrieverSourcePackage
AWSKendraRetrieverVarious AWS resources@langchain/aws

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Setup

You’ll need an AWS account and an Amazon Kendra instance to get started. See this tutorial from AWS for more information. If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below: 
// process.env.LANGSMITH_API_KEY = "<YOUR API KEY HERE>";
// process.env.LANGSMITH_TRACING = "true";

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Installation

This retriever lives in the @langchain/aws package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/aws @langchain/core
</Npm2Yarn>

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Instantiation

Now we can instantiate our retriever: 
import { AmazonKendraRetriever } from "@langchain/aws";

const retriever = new AmazonKendraRetriever({
  topK: 10,
  indexId: "YOUR_INDEX_ID",
  region: "us-east-2", // Your region
  clientOptions: {
    credentials: {
      accessKeyId: "YOUR_ACCESS_KEY_ID",
      secretAccessKey: "YOUR_SECRET_ACCESS_KEY",
    },
  },
});

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Usage

const query = "..."

await retriever.invoke(query);

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Use within a chain

Like other retrievers, the AWSKendraRetriever can be incorporated into LLM applications via chains. We will need a LLM or chat model: 
<ChatModelTabs customVarName="llm" />

// @lc-docs-hide-cell

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  temperature: 0,
});

import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";

import type { Document } from "@langchain/core/documents";

const prompt = ChatPromptTemplate.fromTemplate(`
Answer the question based only on the context provided.

Context: {context}

Question: {question}`);

const formatDocs = (docs: Document[]) => {
  return docs.map((doc) => doc.pageContent).join("\n\n");
}

// See https://js.langchain.com/docs/tutorials/rag
const ragChain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocs),
    question: new RunnablePassthrough(),
  },
  prompt,
  llm,
  new StringOutputParser(),
]);

await ragChain.invoke(query);

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API reference

For detailed documentation of all AmazonKendraRetriever features and configurations head to the API reference.