# Amazon Kendra: A Complete Guide to Enterprise Search

> Source: <https://promptcube3.com/en/threads/2918/>
> Published: 2026-07-24 21:47:21+00:00

# Amazon Kendra: A Complete Guide to Enterprise Search

## The Architecture of Intelligent Search

Kendra differs from standard Elasticsearch or OpenSearch implementations because it's a managed service that handles the heavy lifting of indexing and ranking. It uses a proprietary ML model to index documents, which means you don't have to manually define "boosts" or complex scoring rules for specific fields to get relevant results.

The core workflow follows a strict pipeline:

1. **Data Source Connection**: Connecting to S3, SharePoint, ServiceNow, or Salesforce.

2. **Indexing**: The service crawls the source, extracts text, and builds a semantic index.

3. **Querying**: The user sends a natural language query via the API.

4. **Retrieval**: Kendra returns the most relevant document snippets and, in many cases, a generated answer.

## Deployment and Configuration

Setting up a Kendra index requires choosing between "Developer Edition" (for testing) and "Enterprise Edition" (for production). A critical part of the setup is the **Facet** configuration. Facets allow users to filter results by categories (e.g., filtering by "Department" or "Document Type").

To query Kendra programmatically, you use the `Query`

API. Here is a basic implementation using Boto3 in Python:

``` python
import boto3

kendra = boto3.client('kendra', region_name='us-east-1')

def search_enterprise_docs(query_text, index_id):
    response = kendra.query(
        IndexId=index_id,
        QueryText=query_text
    )
    
    # Extracting the result items
    for result in response.get('ResultItems', []):
        print(f"Title: {result['_id']}")
        print(f"Excerpt: {result['DocumentExcerpt']}")
        print("-" * 20)

# Example usage
# index_id = 'your-kendra-index-id'
# search_enterprise_docs("What is the remote work policy?", index_id)
```

## Real-World Performance and Constraints

In my experience deploying this for internal documentation, the "Answer" feature is the strongest selling point. While standard search returns a list of links, Kendra often provides a concise paragraph answering the question directly.

However, there are specific technical constraints to be aware of:

**Indexing Latency**: Depending on the volume of documents in S3, the initial sync can take hours. Subsequent incremental crawls are faster but not instantaneous.**Cost**: The Enterprise Edition is expensive. For a small organization, the monthly cost of a single index can be a shock if you aren't monitoring usage.**Document Parsing**: While it handles PDFs and HTML well, highly complex tables inside PDFs sometimes lead to "hallucinated" excerpts where the ML model misreads the column alignment.

## Transitioning to LLM Agents

With the rise of [RAG](/en/tags/rag/) (Retrieval-Augmented Generation), the role of Kendra is evolving. Instead of using Kendra as the final user interface, many are now using it as the **retrieval engine** for an LLM agent.

By piping Kendra's `DocumentExcerpt`

into a model like [Claude](/en/tags/claude/) 3.5 or GPT-4, you eliminate the "list of links" experience entirely. The workflow looks like this:`User Query`

→ `Kendra (Retrieve Context)`

→ `LLM (Synthesize Answer)`

→ `User`

.

This AI workflow significantly reduces hallucinations because the LLM is grounded in the specific enterprise data retrieved by Kendra's semantic search, rather than relying on its general training data.

[Next Machine Learning from Scratch: A Beginner's Guide →](/en/threads/2911/)