Semarchy GenAI Amazon Bedrock enricher

The Semarchy GenAI Amazon Bedrock enricher generates a single text response based on a user prompt.

Plugin ID

Semarchy GenAI Amazon Bedrock Enricher - com.semarchy.engine.plugins.genai.amazon.bedrock.simple

Description

The GenAI Amazon Bedrock enricher is designed to enhance text data using language models available through the Amazon Bedrock platform. Users can leverage this enricher to improve the quality, relevance, and depth of their text data using advanced AI capabilities.

Plugin parameters

The following table lists the plugin parameters.

Parameter name Mandatory Type Description

AWS Access Key

Yes

String

Unique identifier for accessing Amazon Bedrock’s functionalities and resources.

AWS Region

Yes

String

Geographic area where the models are hosted, impacting model availability and performance. For more information, see the official AWS documentation.

AWS Secret Access Key

Yes

String

Confidential key used to sign the API calls to Amazon Bedrock’s endpoints.

Model Name

Yes

String

Language model to be used (e.g., amazon.titan-text-express-v1).

Max Tokens

No

Integer

Maximum number of tokens allowed in the generated output during text generation.
Default value: 50

The total length of input and generated tokens is limited by the context length of the model.
Setting an insufficient number of tokens may result in a runtime error during response processing. Make sure to adjust the Max Tokens parameter to accommodate the number of generated tokens, considering your requirements and the terms of your OpenAI license.

Temperature

No

Number

Value ranging from 0 to 1 for balancing between conservative and coherent outputs (0) and creative variations (1) during text generation.
Default value: 0

Top P

No

Number

Value ranging from 0 to 1 for defining the cumulative probability threshold for nucleus sampling (i.e., token selection).

Nucleus sampling is an alternative to temperature sampling. Ignore this parameter if you configured the Temperature parameter.

Stop Sequences

No

Integer

Character sequence acting as a signal for the model to halt the generation of further tokens (e.g., punctuation marks, specific words or phrases, newline characters, etc.).

The final returned text does not include the stop sequence itself.

Language models

Language models are AI systems trained on vast amounts of text data to understand and generate human-like language, enabling tasks like text completion, translation, summarization, and sentiment analysis.

Amazon Bedrock offers a range of models with distinct capabilities. The GenAI Amazon Bedrock enricher supports the amazon.titan-text-express-v1 model, which is optimized for fast text generation.

For more information on Amazon Titan models and a list of stable model versions available on Amazon Bedrock, see the official Amazon Bedrock documentation.

Tokens

Tokens are units of text that language models use to process and generate language. They can range from individual characters to entire words, depending on the language and the specific model being used.

For more information about tokens, see the official AWS documentation or this video from the official AWS Developers channel on YouTube.

Plugin inputs

The following table lists the plugin inputs.

Input name Mandatory Type Description

User Prompt

Yes

String

Input query specifying the user’s particular needs, intentions, or requests for the generated text.

System Prompt

No

String

Initial instruction designed to guide the model towards specific topics, styles, tones, or formats of generated text.

Plugin outputs

The following table lists the plugin outputs.

Output name Type Description

Response

String

Generated response applied to a designated attribute.

Examples and use cases

Social media blurb generation: summarizing product descriptions

Imagine a scenario where a user needs a condensed version of a detailed product description for social media, where character or space limits apply. In practice, during record creation or editing, the user wants a brief social media blurb to be generated based on the Description field’s content.

For instance, consider the Velocity Pro Carbon Bike product record with the following description:
"Introducing the Velocity Pro Carbon Bike from Italy, a high-performance masterpiece meticulously crafted for speed enthusiasts. This aerodynamic marvel boasts cutting-edge design and lightweight materials, providing an unparalleled riding experience. Elevate your cycling adventures with the pinnacle of Italian engineering. Price: 134."

Suppose the enricher is configured as follows:

  • In the plugin input properties:

    • User Prompt: 'Summarize the following description in less than 150 characters: ' || Description

  • In the plugin output properties:

    • Attribute Name: SocialMediaBlurb

In this setup, the response provided by the enricher may populate the Social Media Blurb field of the Velocity Pro Carbon Bike record with the following:
"Introducing the Velocity Pro Carbon Bike from Italy: a high-performance marvel, aerodynamic design, lightweight materials, ultimate riding experience. Price: $134."

Additional use cases

Below are just a few examples of the diverse range of use cases that the GenAI Amazon Bedrock enricher can support:

  • Content generation: create detailed descriptions of products or customer personas; generate captions for social media posts, ads, or campaigns.

  • Content synthesis: capture the essence of a text to create titles or names for products, services, or features.

  • Content quality: enhance clarity, refine structure, and adjust tone to meet specific communication goals; correct spelling and grammar errors to adhere to standard language conventions.

  • Custom tone of voice: adapt the tone of the text to align with specific communication objectives, conveying neutrality, professionalism, empathy, education, or casualness.

  • Language translation: translate text into one or several other languages.

  • Price conversion: convert product prices from one currency to another.