IBM watsonx.ai is part of IBM's AI and data platform. It allows you to work with generative AI and traditional machine learning models, providing tools to build and refine models for your specific needs. It offers various models, including options from IBM, open-source models, and the ability to use your own.
Building a flow in IBM App Connect with IBM watsonx.ai
Use App Connect to build flows that integrate with IBM watsonx.ai and other applications. The connector is displayed as IBM watsonx.ai on the IBM App Connect User Interface (UI) and the IBM Automation Explorer UI.
To allow App Connect to connect to your IBM watsonx.ai account, you need to fill in the connection fields that you see in the App Connect Designer Catalog page or flow editor.
Supported authorization types:
For detailed information about different authorization types, a list of supported actions and events, how to generate connection field values, and how to use the Templates gallery, see How to use IBM App Connect with IBM watsonx.ai on the IBM Documentation webpage.
Supported objects in IBM watsonx.ai
Object |
Description |
Email |
The Email object in IBM Watsonx.ai is a data structure used to represent an email message. You can use it to generate text content for email drafting. |
Foundation models |
The Foundation models object in IBM Watsonx.ai is a pre-trained artificial intelligence (AI) model that serves as a foundation for building custom AI models. You can use the actions to retrieve a list of foundation models and the tasks supported by the models. |
Sentiment analysis |
The Sentiment analysis object in IBM Watsonx.ai is a natural language processing (NLP) model that analyzes text to determine the sentiment or emotional tone behind it. It can identify whether the sentiment is positive, negative, or neutral, and provide a confidence score for the analysis. |
Summary |
The Summary object in IBM Watsonx.ai is a natural language processing (NLP) model that condenses large amounts of text into a concise and meaningful summary. It uses advanced algorithms to identify the most important information and key points in the text and generates a summary that captures the main ideas and concepts |
Text embeddings |
The Text embedding object in IBM Watsonx.ai is a natural language processing (NLP) model that converts text into a numerical representation, known as an embedding, that captures the semantic meaning and context of the text. This allows for efficient and effective comparison, clustering, and analysis of text data. |
Text generation |
The Text generation object in IBM Watsonx.ai is a natural language processing (NLP) model that generates human-like text based on a given prompt, topic, or style. It uses advanced algorithms to predict and generate text that is coherent, contextually relevant, and engaging. |
Tokenization |
The Tokenization object in IBM Watsonx.ai is a natural language processing (NLP) model that breaks down text into individual words or tokens, such as words, phrases, or symbols. This process, known as tokenization, is a fundamental step in text analysis and is used to prepare text data for further processing and analysis.
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Scenario 1: Summarize the open leads from Salesforce using IBM watsonx.ai and send an email
Consider this scenario: When the flow is triggered, it detects new users in Salesforce, retrieves a list of leads based on certain criteria, and then sends the information to IBM watsonx.ai to be summarized. Finally, the user is notified through an email.
The following steps are involved in the flow:
Scenario 2: Send an SMS notification about upcoming offers for Shopify customers using text from IBM watsonx.ai
Consider this scenario: When the flow is triggered, it detects customers in Shopify from which contacts are created in the ClickSend contact list, and IBM watsonx.ai generates text for SMS campaigns targeting these contacts.
The following steps are involved in the flow: