Kendra

Amazon Kendra intelligent enterprise search service for indexing and querying documents with natural language understanding.

Configuration

Property	Value
Protocol	AwsJson1_1
Signing Name	kendra
Target Prefix	kendra
Persistence	No

Quick Start

Create an index, add documents, and search them:

# Create an index
INDEX_ID=$(curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.CreateIndex" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d '{"Name":"my-search-index","RoleArn":"arn:aws:iam::000000000000:role/KendraRole","Edition":"DEVELOPER_EDITION"}' \
  | jq -r '.Id')

echo "Index ID: $INDEX_ID"

# Add documents
curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.BatchPutDocument" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d "{\"IndexId\":\"$INDEX_ID\",\"Documents\":[{\"Id\":\"doc1\",\"Title\":\"What is Kendra?\",\"Blob\":\"$(echo -n 'Amazon Kendra is an intelligent enterprise search service powered by machine learning.' | base64)\"},{\"Id\":\"doc2\",\"Title\":\"How to use Kendra\",\"Blob\":\"$(echo -n 'To use Kendra, create an index, add documents, and query using natural language.' | base64)\"}]}"

# Search
curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.Query" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d "{\"IndexId\":\"$INDEX_ID\",\"QueryText\":\"intelligent search\"}"

Operations

Index Management

• CreateIndex — create a new Kendra index

  • Input: Name (required), RoleArn (IAM role ARN), Edition (DEVELOPER_EDITION or ENTERPRISE_EDITION), optional Description, Tags
  • Returns: Id (UUID for the index)
  • Index status transitions to ACTIVE immediately in AWSim

• DescribeIndex — get index details and status

  • Input: Id
  • Returns: Name, Id, Status (ACTIVE), Edition, DocumentMetadataConfigurations, IndexStatistics

• ListIndices — list all indexes in the account/region

  • Input: optional NextToken, MaxResults
  • Returns: paginated IndexConfigurationSummaryItems

• DeleteIndex — delete an index and all its documents

  • Input: Id

• UpdateIndex — update index name, description, or capacity

  • Input: Id, optional Name, Description, CapacityUnits

Data Sources

• CreateDataSource — create a data source connector

  • Input: IndexId, Name, Type (S3, SHAREPOINT, DATABASE, SALESFORCE, CONFLUENCE, CUSTOM), Configuration, RoleArn
  • Returns: Id

• ListDataSources — list data sources for an index

  • Input: IndexId, optional NextToken, MaxResults

• DeleteDataSource — delete a data source

Documents

• BatchPutDocument — add or update documents in an index

  • Input: IndexId, Documents (list of {Id, Title, Blob (base64 content), ContentType, S3Path, Attributes})
  • Returns: FailedDocuments (list of failed document IDs and reasons)

• BatchDeleteDocument — remove documents from an index by ID

  • Input: IndexId, DocumentIdList (list of document IDs)
  • Returns: FailedDocuments

Search

• Query — search the index with a natural language or keyword query

  • Input: IndexId, QueryText (required), optional AttributeFilter, Facets, RequestedDocumentAttributes, QueryResultTypeFilter (DOCUMENT, ANSWER, QUESTION_ANSWER), PageNumber, PageSize
  • Returns: ResultItems (list with Id, Type, DocumentTitle, DocumentExcerpt, DocumentId, ScoreAttributes), TotalNumberOfResults

• Retrieve — retrieve specific passages from documents matching a query

  • Input: IndexId, QueryText, optional AttributeFilter, RequestedDocumentAttributes, PageNumber, PageSize
  • Returns: ResultItems (list with Id, DocumentId, DocumentTitle, Content (passage text), DocumentAttributes)

• SubmitFeedback — submit relevance feedback to improve search results

  • Input: IndexId, QueryId, ClickFeedbackItems or RelevanceFeedbackItems

Curl Examples

# 1. Create an index
curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.CreateIndex" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d '{"Name":"docs-index","RoleArn":"arn:aws:iam::000000000000:role/KendraRole","Edition":"DEVELOPER_EDITION","Description":"Documentation search index"}'

# 2. Add plain text documents
CONTENT=$(echo -n "AWS Lambda lets you run code without provisioning or managing servers." | base64)
curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.BatchPutDocument" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d "{\"IndexId\":\"YOUR_INDEX_ID\",\"Documents\":[{\"Id\":\"lambda-intro\",\"Title\":\"Lambda Introduction\",\"Blob\":\"$CONTENT\",\"ContentType\":\"PLAIN_TEXT\"}]}"

# 3. Search with Retrieve (passage-level)
curl -s http://localhost:4566 \
  -H "Content-Type: application/x-amz-json-1.1" \
  -H "X-Amz-Target: kendra.Retrieve" \
  -H "Authorization: AWS4-HMAC-SHA256 Credential=test/20260421/us-east-1/kendra/aws4_request, SignedHeaders=host, Signature=fake" \
  -d '{"IndexId":"YOUR_INDEX_ID","QueryText":"serverless compute"}'

SDK Example

import {
  KendraClient,
  CreateIndexCommand,
  BatchPutDocumentCommand,
  QueryCommand,
} from '@aws-sdk/client-kendra';

const kendra = new KendraClient({
  region: 'us-east-1',
  endpoint: 'http://localhost:4566',
  credentials: { accessKeyId: 'test', secretAccessKey: 'test' },
});

// Create index
const { Id: indexId } = await kendra.send(new CreateIndexCommand({
  Name: 'product-docs',
  RoleArn: 'arn:aws:iam::000000000000:role/KendraRole',
  Edition: 'DEVELOPER_EDITION',
}));

// Add documents
await kendra.send(new BatchPutDocumentCommand({
  IndexId: indexId!,
  Documents: [
    {
      Id: 'getting-started',
      Title: 'Getting Started Guide',
      Blob: Buffer.from('This guide covers installation, configuration, and first steps.'),
      ContentType: 'PLAIN_TEXT',
    },
    {
      Id: 'api-reference',
      Title: 'API Reference',
      Blob: Buffer.from('Comprehensive reference for all API endpoints and parameters.'),
      ContentType: 'PLAIN_TEXT',
    },
  ],
}));

// Search
const { ResultItems, TotalNumberOfResults } = await kendra.send(new QueryCommand({
  IndexId: indexId!,
  QueryText: 'how to get started',
  PageSize: 10,
}));

console.log('Total results:', TotalNumberOfResults);
ResultItems?.forEach(item => {
  console.log(`- ${item.DocumentTitle?.Text}: ${item.DocumentExcerpt?.Text}`);
});

Behavior Notes

• AWSim's Kendra uses substring-based matching for search — documents containing the query terms are returned.
• Query results are ranked by number of matched terms (not semantic similarity or BM25).
• Retrieve returns passage-level excerpts from matching documents (substring of the document content).
• Index status transitions to ACTIVE immediately after creation — no indexing delay.
• Document Blob content is base64-encoded; use ContentType: "PLAIN_TEXT" for text documents.
• State is in-memory only and lost on restart.