This new feature allows t6 IoT users to enhance their automation workflows by directly integrating OpenAI's powerful language model responses into their Rule Engine flows. Users can input a text value as well as custom prompt, for generating an OpenAI response. The generated response is then substituted for the user's original input, providing AI-enhanced data that can be used for further processing or actions.

Tagged on #feature, #AI-Flex, #Artificial Intelligence, #AI, #NLP, #NLU, #rules,

AI-Flex: AI-Powered Value Substitution

AI-Flex expands t6’s capabilities by combining IoT automation with advanced language understanding. It enables more intelligent, context-aware rule-based actions, transforming user workflows in meaningful ways.

How AI-Flex Works

1. User Input as Prompt: The user enters a text value in the t6 IoT platform, serving as an initial message including an optional “prompt” for the OpenAI API.
2. OpenAI Chat Completion: The t6 Rule Engine sends this prompt to the OpenAI API as a request for completion. OpenAI processes the input and returns a context-based response.
3. Value Substitution: Once OpenAI generates a response, AI-Flex replaces the original user input with this AI-driven text. The AI-enhanced response becomes the new value in the Rule Engine flow.
4. Further Actions: The substituted value (OpenAI’s response) can trigger additional actions within t6’s Rule Engine, such as sending notifications, activating devices, or logging data.

Customizable Options in AI-Flex

• Prompt Customization: Users can define their own prompt text for tailored and context-specific responses.
• Rule Engine Conditions: Users can set conditions that control when AI-Flex calls the OpenAI API, such as specifying certain parameters that must be met.
• API Parameters: Advanced users may also customize OpenAI API parameters (e.g., temperature, max tokens) tailoring responses to specific needs.

Expected Benefits of AI-Flex

• Automated Insights: Users can generate dynamic and context-aware insights from their data, thanks to OpenAI’s language capabilities. For example, a user might input a data summary, and OpenAI could respond with an analysis or interpretation.
• Enhanced Automation: By allowing OpenAI responses to substitute user input, AI-Flex enables a new range of automation possibilities. For instance, the Rule Engine could take actions based on sentiment analysis, summaries, or even generated instructions from OpenAI.
• Greater Flexibility: AI-Flex offers flexibility for users seeking to leverage AI within their IoT workflows, creating a bridge between sensor data and advanced language models.

Example Use Case for AI-Flex

A company using t6 to monitor customer feedback could post customer comments into the system. Using AI-Flex, the Rule Engine sends the comment to OpenAI, which responds with a sentiment analysis summary. This substituted sentiment summary then triggers specific actions in t6, such as flagging certain feedback for closer review or sending an alert if the sentiment is highly negative.