Skip to main content
Version: v0.16.0

Generate component

The component that prompts the LLM to respond appropriately.

A Generate component fine-tunes the LLM and sets its prompt.

Scenarios​

A Generate component is essential when you need the LLM to assist with summarizing, translating, or controlling various tasks.

Configurations​

Model​

Click the dropdown menu of Model to show the model configuration window.

  • Model: The chat model to use.
    • Ensure you set the chat model correctly on the Model providers page.
    • You can use different models for different components to increase flexibility or improve overall performance.
  • Freedom: A shortcut to Temperature, Top P, Presence penalty, and Frequency penalty settings, indicating the freedom level of the model.
    This parameter has three options:
    • Improvise: Produces more creative responses.
    • Precise: (Default) Produces more conservative responses.
    • Balance: A middle ground between Improvise and Precise.
  • Temperature: The randomness level of the model's output.
    Defaults to 0.1.
    • Lower values lead to more deterministic and predictable outputs.
    • Higher values lead to more creative and varied outputs.
    • A temperature of zero results in the same output for the same prompt.
  • Top P: Nucleus sampling.
    • Reduces the likelihood of generating repetitive or unnatural text by setting a threshold P and restricting the sampling to tokens with a cumulative probability exceeding P.
    • Defaults to 0.3.
  • Presence penalty: Encourages the model to include a more diverse range of tokens in the response.
    • A higher presence penalty value results in the model being more likely to generate tokens not yet been included in the generated text.
    • Defaults to 0.4.
  • Frequency penalty: Discourages the model from repeating the same words or phrases too frequently in the generated text.
    • A higher frequency penalty value results in the model being more conservative in its use of repeated tokens.
    • Defaults to 0.7.
  • Max tokens: Sets the maximum length of the model's output, measured in the number of tokens.
    • Defaults to 512.
    • If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
NOTE
  • It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
  • If you are uncertain about the mechanism behind Temperature, Top P, Presence penalty, and Frequency penalty, you can simply choose one of the three options of Freedom.

System prompt​

Typically, you use the system prompt to describe the task for the LLM, specify how it should respond, and outline other miscellaneous requirements. We do not plan to elaborate on this topic, as it can be as extensive as prompt engineering. However, please be aware that the system prompt is often used in conjunction with keys (variables), which serve as various data inputs for the LLM.

Keys in a system prompt should be enclosed in curly braces. Below is a prompt excerpt of a Generate component from the Interpreter template (component ID: Reflect):

Your task is to read a source text and a translation to {target_lang}, and give constructive suggestions to improve the translation. The source text and initial translation, delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT> and <TRANSLATION></TRANSLATION>, are as follows:

<SOURCE_TEXT>
{source_text}
</SOURCE_TEXT>

<TRANSLATION>
{translation_1}
</TRANSLATION>

When writing suggestions, pay attention to whether there are ways to improve the translation's fluency, by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions.
- Each suggestion should address one specific part of the translation.
- Output the suggestions only.

Where {source_text} and {target_lang} are global variables defined by the Begin component, while {translation_1} is the output of another Generate component with the component ID Translate directly.

Cite​

This toggle sets whether to cite the original text as reference.

NOTE

This feature is used for multi-turn dialogue only and is applicable only when the original documents are uploaded to a knowledge base and have finished file parsing.

Message window size​

An integer specifying the number of previous dialogue rounds to input into the LLM. For example, if it is set to 12, the tokens from the last 12 dialogue rounds will be fed to the LLM. This feature consumes additional tokens.

IMPORTANT

This feature is used for multi-turn dialogue only.

Key (Variable)​

IMPORTANT

A Generate component relies on keys (variables) to specify its data inputs. Its immediate upstream component is not necessarily its data input, and the arrows in the workflow indicate only the processing sequence.

variable_settings

Keys in a Generate component are used in conjunction with the system prompt to specify data inputs for the LLM. As shown in the above screenshot, the values are categorized into two groups:

  • Component Output: The value of the key should be a component ID.
  • Begin Input: The value of the key should be the name of a global variable defined in the Begin component.

Examples​

You can explore our three-step interpreter agent template, where a Generate component (component ID: Reflect) takes three global variables:

  1. Click the Agent tab at the top center of the page to access the Agent page.
  2. Click + Create agent on the top right of the page to open the agent template page.
  3. On the agent template page, hover over the Interpreter card and click Use this template.
  4. Name your new agent and click OK to enter the workflow editor.
  5. Click on component Reflect, to display its Configuration window, where:
    • {target_lang} and {source_text} are defined in the Begin component and require user input.
    • {translation_1} is the output from the upstream component Translate directly.