ManualTrigger Automate

Target Audience

This workflow is designed for:

• Developers: Who want to automate chat message handling with advanced logic.
• Businesses: Looking to enhance customer interactions via chat applications.
• Data Scientists: Interested in extracting and consolidating information efficiently.
• Product Managers: Who need insights from chat data to improve user experience.

It is particularly beneficial for teams that require real-time message processing and response management.

Problem Solved

This workflow addresses the challenge of managing chat messages effectively by:

• Consolidating Messages: Collecting and merging messages to avoid duplication.
• Managing Response Timing: Automatically determining wait times based on message length and context.
• Handling Concurrent Requests: Preventing overlapping responses which can confuse users.
• Storing Temporary Data: Using Redis to manage message buffers and state, ensuring no messages are lost during processing.

Workflow Steps

1. Manual Trigger: The workflow begins with a manual trigger, allowing users to start the process when needed.
2. Receive Chat Message: It listens for incoming chat messages and captures relevant data such as context_id and message.
3. Store Incoming Message: The message is pushed into a Redis list for temporary storage, ensuring that it is not lost.
4. Update Metadata: It sets the last_seen timestamp and increments the buffer_count to track how many messages are waiting.
5. Check for Waiting Responses: The workflow checks if there is already a response in progress to avoid duplicate processing.
6. Calculate Wait Time: It calculates the wait time based on the message length to determine how long to wait before processing the messages.
7. Inactivity Check: It verifies if the last message was received within a certain threshold to decide whether to proceed with processing.
8. Consolidate Messages: If conditions are met, it retrieves all buffered messages, consolidates them, and prepares a response using an AI model.
9. Respond to User: Finally, it sends the consolidated response back to the chat interface, ensuring a seamless user experience.