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Output

Display results of your processing.

What is Output?​

The Output serves as a terminal in your workflow, designed to display processed text or results from a Large Language Model (LLM) chain. It can handle various output formats including text, JSON, and files, making it versatile for different use cases.

How to use it?​

The Output is straightforward to use. Follow these steps to integrate it into your workflow:

  1. Add Output:

    • Drag and drop the Output into your workflow canvas from the Output category.

  2. Connect Input Anchors:

    • Link the output of the preceding node (e.g., an API Action or LLM) to the Input anchor of the Output.
    • The Output accepts different types of data inputs: Text, JSON, and File.

  3. Configure Input Anchors:

    • Select the type of data you are expecting (Text, JSON, or File) by configuring the input options.

Features​

  • It displays markdown as formatted output.
  • The Output can be configured to display JSON or file outputs. This is useful when dealing with more complex data structures or when you need to visualize file outputs directly.

Example Task: Text Summarization Workflow​

Objective: Create a workflow that accepts user-provided text and produces a concise summary.

Step-by-Step Setup​

  1. Add Text Input:

    • Add a Text Input node to your canvas.
    • Add an input variable.
    • Edit the template text to be: Summarize the following text:
    • Add an input variable and click the created variable to make use of it in the text. For more details on this step see the text input documentation.

  2. Add Input node

    • Add an input node.
    • Change its name to ArticleText
    • Change the type to text.

  3. Add Large Language Model:

    • Locate the Large Language Model node in your node selection.
    • Add it to the canvas.
    • Configure it with your API key and select an appropriate model.

  4. Add Output:

    • Add a String Output component to display the result.
    • Connect the output of the OpenAI component to the input of the String Output.

  5. Connect Components:

    • Link the output of the "ArticleText" Input node to the created variable in the text input node.
    • Link the text input node output to the Prompt Text of the Large Language Model node.
    • Link the Large Language Model output to the Output node.

This leaves us with the result:

Article summary graph
🔗 Related Nodes: Input, Text, Large Language Model, Output

Execution​

To execute the workflow described in the step by step guide, add your article to the input nodes value field.

Additional Information​

  • Ensure your preceding nodes (like OpenAI LLM or API Action) are correctly configured to generate the expected data type.
  • The Output does not require any additional input parameters, making it a plug-and-play solution for displaying results.

By following these steps, you can effectively utilize the Output to display results from various processing nodes in your workflow.