Using Amazon Nova models in Elasticsearch

In this article, we will discuss Amazon's AI model family, Amazon Nova, and learn how to use it alongside Elasticsearch.

About Amazon Nova

Amazon Nova is a family of Amazon artificial intelligence models, available on Amazon Bedrock and designed to offer high performance and cost efficiency. These models operate with text, image and video inputs, generate textual outputs, and are optimized for different accuracy, speed and cost needs.

Amazon Nova main models

Amazon Nova Micro: Focused exclusively on text, this is a fast and cost-effective model, ideal for translation, reasoning, code completion and solving mathematical problems. Its generation exceeds 200 tokens per second, making it ideal for applications that require instant responses.
Amazon Nova Lite: A low-cost multimodal model capable of quickly processing images, videos and texts. It stands out for its speed and accuracy, being indicated for interactive and high-volume applications where cost is a relevant factor.
Amazon Nova Pro: The most advanced option, combining high accuracy, speed and cost efficiency. Ideal for complex tasks such as video summarization, questions and answers, software development and AI agents. Expert reviews attest to its excellence in textual and visual comprehension, as well as its ability to follow instructions and execute automated workflows.

Amazon Nova models are suitable for a variety of applications, from content creation and data analysis to software development and AI-powered process automation.

Below, we’ll demonstrate how to use Amazon Nova models in conjunction with Elasticsearch for automated product review analysis.

What we will do:

Create an endpoint via Inference API, integrating Amazon Bedrock with Elasticsearch.
Create a pipeline using the Inference Processor, which will make calls to the Inference API endpoint.
Index product reviews and automatically generate an analysis of the reviews using the pipeline.
Analyze the results of the integration.

Creating an Endpoint in the Inference API

First, we configure the Inference API to integrate Amazon Bedrock with Elasticsearch. We define Amazon Nova Lite, id amazon.nova-lite-v1:0, as the model to use since it offers a balance between speed, accuracy, and cost.

Note: You will need valid credentials to use Amazon Bedrock. You can see the documentation for obtaining access keys here:

Creating the review analysis pipeline

Now, we create a processing pipeline that will use the Inference Processor to execute a review analysis prompt. This prompt will send the review data to Amazon Nova Lite, which will perform:

Sentiment classification (positive, negative, or neutral).
Review summarization.
Keywords generation.
Authenticity measurement (authentic | suspicious | generic).

Indexing reviews

Now, we index product reviews using the Bulk API. The pipeline created earlier will be automatically applied, adding the analysis generated by the Nova model to the indexed documents.

Querying and analyzing the results

Finally, we run a query to see how the Amazon Nova Lite model analyzes and classifies the reviews. By running GET products/_search, we get the documents already enriched with the fields generated from the review content.

The model identifies the predominant sentiment (positive, neutral, or negative), generates concise summaries, extracts relevant keywords, and estimates the authenticity of each review. These fields help understand the customer’s opinion without having to read the full text.

To interpret the results, we look at:

Sentiment, which indicates the consumer’s overall perception of the product.
The summary, which highlights the main points mentioned.
Keywords, which can be used to group similar reviews or identify feedback patterns.
Authenticity, which signals whether the review seems trustworthy. This is useful for curation or moderation.

Final Thoughts

The integration between Amazon Nova Lite and Elasticsearch demonstrated how language models can transform raw reviews into structured and valuable information. By processing the reviews through a pipeline, we were able to extract sentiment, authenticity, summaries, and keywords automatically and consistently.

The results show that the model can understand the context of the reviews, classify user opinions, and highlight the most relevant points of each experience. This creates a much richer dataset that can be leveraged to improve search capabilities.

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