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Version: DEV

Keyword component

A component that extracts keywords from a user query.

A Keyword component uses the specified LLM to extract keywords from a user query.

Scenarios​

A Keyword component is essential where you need to prepare keywords for a potential keyword search.

Configurations​

Input​

The Keyword component relies on input variables to specify its data inputs (queries). Click + Add variable in the Input section to add the desired input variables. There are two types of input variables: Reference and Text.

  • Reference: Uses a component's output or a user input as the data source. You are required to select from the dropdown menu:
    • A component ID under Component Output, or
    • A global variable under Begin input, which is defined in the Begin component.
  • Text: Uses fixed text as the query. You are required to enter static text.

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.

Number of keywords​

An integer specifying the number of keywords to extract from the user query. Defaults to 3. Please note that the number of extracted keywords depends on the LLM's capabilities and the token count in the user query, and may not match the integer you set.

Examples​

Explore our general-purpose chatbot agent template, where the Keyword component (component ID: keywords) is used to extract keywords from financial inputs for a potential stock search in the akshare component:

  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 General-purpose chatbot card and click Use this template.
  4. Name your new agent and click OK to enter the workflow editor.
  5. Click on the Keyword component to display its Configuration window.