Generated Speech Node
The Generated Speech Node is a fundamental component of the drag-n-drop editor for digital personas operated by Conversational AI. This node allows you to generate speech or text within a dialogue. It's a powerful tool for crafting dynamic and engaging conversations. In this documentation, we'll explore the various aspects of the Generated Speech Node, including its configuration options.
The Generated Speech Node is designed to generate text or speech based on the provided input prompt. It leverages advanced language models, such as ChatGPT and GPT-4, to generate responses that can be used in your conversational applications. Underneath, it uses llm.chat function.
Prompt Panel
The "Prompt" configuration panel is where you define the input that guides the node's response generation. You can write a detailed prompt describing what the node should generate in the dialogue. The quality and relevance of your prompt will greatly influence the generated output.
System Panel
The "System" panel is where you define the system prompt that describes the properties of the overall conversation or character of digital persona.
Config Panel
The "Config" panel contains several important settings that allow you to fine-tune the behavior of the Generated Speech Node. Here are the key configuration options:
Task Name
An optional parameter that enables you to specify the task performed by the node, facilitating analysis.
Number of turns
The number of dialogue turns provided as input to the language model.
Model Selection Dropdown
Choose between different language models for text generation. Options typically include "ChatGPT" and "GPT-4." Select the model that best suits your specific use case.
Temperature
Adjust the "temperature" parameter to control the randomness of the generated text. Higher values (e.g., 0.8) result in more randomness, while lower values (e.g., 0.2) make the output more deterministic.
Max Tokens
Set a limit on the maximum number of tokens (words or characters) in the generated output. This helps you control the length of responses.
Top P
Control the diversity of the generated output by specifying a threshold for the cumulative probability of the most likely tokens. A higher value, such as 0.9, allows for more diversity, while a lower value, like 0.2, results in more focused responses.
Frequency Penalty
Adjust the frequency penalty to discourage the model from repeating the same phrases or words too often. This helps in producing varied and natural-sounding responses. A higher frequency penalty value will result in the model being more conservative in its use of repeated tokens.
Presence Penalty
Set the presence penalty to encourage the model to include a diverse range of tokens in the generated text. A higher presence penalty value will result in the model being more likely to generate tokens that have not yet been included in the generated text.
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