AWS Amazon Kendra
Browse all AWS APIs★ Only Publicly Available OpenAPI DocumentAI/MLEmbeddings Searchhmac65 EndpointsREST
For Agents
Run natural-language enterprise search across documents in S3, SharePoint, Confluence, and other sources with passage-level answers and access controls.
Use for: I need to search across our enterprise documents, Get an answer from indexed PDFs, List all data sources attached to a Kendra index, Set up a SharePoint connector
Not supported: Does not handle web crawling of public sites, vector index management, or LLM generation — use for permission-aware enterprise document search and retrieval only.
Jentic publishes the only available OpenAPI specification for Amazon Kendra, keeping it validated and agent-ready. Amazon Kendra is a managed enterprise search service that ingests documents from S3, SharePoint, Confluence, Salesforce, ServiceNow, and many other sources, then answers natural-language queries with passage-level results. Its 65 endpoints cover index lifecycle, data source connectors, query and suggestion APIs, FAQ ingestion, access control mappings for tenant-aware results, query suggestions, and featured-results experiences. Kendra is a frequent retrieval layer for enterprise RAG pipelines that need permission-aware document search.
Install Jentic One Beta
Connect the Amazon Kendra to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Kendra, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Amazon Kendra API.
Provision Kendra indexes via CreateIndex
Connect data sources such as S3, SharePoint, and Confluence with CreateDataSource
Submit natural-language queries against an index with the Query operation
Ingest FAQ files for direct answer surfacing
Manage access control list mappings for permission-aware search results
Configure query suggestions and featured results sets
Index custom documents directly via BatchPutDocument
Use Cases
Patterns agents use Amazon Kendra API for, with concrete tasks.
★ Enterprise Document Search
Provide employees with natural-language search across internal documents stored in many systems. Amazon Kendra connects to S3, SharePoint, Confluence, and Salesforce, indexes the content with semantic understanding, and returns ranked answers, document excerpts, and relevant passages. The Query API supports filters, sorting, and access control so each user sees only documents they are entitled to view.
Submit the query 'what is our parental leave policy' against index 12345-abcde and return the top three excerpts
RAG Retrieval Layer
Use Kendra as the retrieval layer for retrieval-augmented generation pipelines that feed an LLM with permission-aware passages. The Query API and the Retrieve API return relevant text spans tagged with their source URI and access tokens, so the application can stitch them into a prompt and cite results back. Kendra's deep relevance ranking and ACL inheritance reduce the engineering effort compared to building an embedding pipeline from scratch.
Call the Retrieve operation for query 'database migration runbook' against index 12345-abcde with userContext groups ['platform']
Customer Support Knowledge Base
Power a self-service support experience by indexing product manuals, help-centre articles, and FAQs. Kendra's FAQ ingestion lets you upload curated question and answer files that surface as direct answers when a user query matches. The Suggestions API offers autocomplete based on actual queries, helping users land on the right article quickly.
Create an FAQ named 'billing-faq' on index 12345-abcde from S3 file s3://docs/faqs/billing.csv with role arn:aws:iam::123:role/kendra-faq
AI Agent Knowledge Retrieval
AI agents call Amazon Kendra through Jentic to ground answers in authoritative enterprise documents before responding. The agent searches Jentic for the right Kendra operation, loads the schema, and submits a query alongside user context tokens for ACL filtering. This adds factual grounding to agent answers without exposing AWS credentials to the agent runtime.
Search Jentic for 'enterprise document search', load the Query schema, and run query 'expense report deadline' against index 12345-abcde
Key Endpoints
65 endpoints — jentic publishes the only available openapi specification for amazon kendra, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateIndex
Create a Kendra index
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateDataSource
Create a data source connector
POST
/#X-Amz-Target=AWSKendraFrontendService.Query
Run a natural-language query
POST
/#X-Amz-Target=AWSKendraFrontendService.Retrieve
Retrieve passages for RAG pipelines
POST
/#X-Amz-Target=AWSKendraFrontendService.BatchPutDocument
Index custom documents directly
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateFaq
Create an FAQ from a curated file
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateIndex
Create a Kendra index
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateDataSource
Create a data source connector
POST
/#X-Amz-Target=AWSKendraFrontendService.Query
Run a natural-language query
POST
/#X-Amz-Target=AWSKendraFrontendService.Retrieve
Retrieve passages for RAG pipelines
POST
/#X-Amz-Target=AWSKendraFrontendService.BatchPutDocument
Index custom documents directly
POST
/#X-Amz-Target=AWSKendraFrontendService.CreateFaq
Create an FAQ from a curated file
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AWS access keys for Kendra are stored encrypted in the Jentic vault. Jentic performs SigV4 signing at execution time, so raw AWS secrets never enter the agent's context.
Intent-based discovery
Agents search Jentic by intent such as 'search enterprise documents' or 'retrieve passages for rag' and Jentic returns the matching Kendra operation with its input schema, so the agent calls the right endpoint without parsing AWS docs.
Time to first call
Direct Kendra integration: 1-3 days to wire SigV4, IAM, and connector setup. Through Jentic: under 1 hour — search, load the schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon Kendra API through Jentic.
Why is there no official OpenAPI spec for Amazon Kendra?
AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Kendra via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does the Amazon Kendra API use?
Amazon Kendra uses AWS Signature Version 4 (SigV4) HMAC request signing. Each call must be signed with an access key, secret key, and optional session token scoped to Kendra IAM permissions. Through Jentic, credentials are stored encrypted in the vault and signing happens at execution time.
Can I use Amazon Kendra as the retrieval layer for an LLM?
Yes. The Retrieve operation is designed for retrieval-augmented generation. It returns longer passages with source attribution rather than the short excerpts returned by Query, which makes it well suited for stitching grounded context into LLM prompts.
How do I run a natural-language query through Jentic?
Search Jentic for 'enterprise document search', load the schema for the Query operation, submit your IndexId and QueryText, then read the ResultItems list for ranked answers and excerpts. Jentic handles SigV4 signing automatically.
What are the rate limits for the Amazon Kendra API?
Per-region quotas apply, including a default queries-per-second limit per index that varies by edition (Developer vs Enterprise) and additional limits on data source sync jobs and document ingestion. Check the Kendra service quotas in the AWS console for the values in your account.
Can Amazon Kendra enforce document-level permissions?
Yes. Pass UserContext with user and group tokens on every Query or Retrieve call, and ensure the indexed documents carry _user_id and _group_ids metadata or are sourced through connectors that propagate ACLs. Kendra filters results so each user only sees documents they are entitled to view.
Is Amazon Kendra free to use?
No. Kendra is billed per index hour with separate Developer and Enterprise editions and additional charges for connector sync jobs and document storage. Use the AWS pricing page to estimate cost based on your index size and query volume.
GET STARTED