Descripción del componente
Cómo funciona el componente Generador
Generator Component Overview
The Generator component is designed to create text responses using a user-provided prompt and a selected Large Language Model (LLM). It is a central part of AI workflows where natural language generation or augmentation is required.
What Does the Generator Do?
This component takes an input prompt (text) and optionally images or a system message, then sends these inputs to a chosen LLM to generate a relevant text-based output. It is versatile and can be used for automating content creation, generating conversational replies, or enriching data with AI-generated language.
The Generator component accepts the following inputs:
Input Name | Type | Description | Required | Advanced |
---|
Input | Message (text) | The main prompt or message to send to the LLM. | No | No |
LLM | Model (BaseChatModel) | The language model used for text generation. | No | No |
Images | FlowSessionAttachmentResponse | Optional images to include as context for the model. | No | No |
System Message | String (multiline text) | An optional system-level instruction for the model (advanced). | No | Yes |
- Input is the primary text prompt you want the model to respond to.
- LLM lets you choose which large language model to use for generation.
- Images can provide additional context if the model supports multimodal inputs.
- System Message is an advanced feature for setting model behavior, such as tone or role.
Outputs
The Generator component produces:
Output Name | Type | Description |
---|
Message Output | Message | The generated text from the LLM |
The output is a text-based message, ready for use in subsequent workflow steps, UI display, or further processing.
Why Use the Generator Component?
- Flexible AI Text Generation: Easily generate natural language responses, explanations, summaries, or creative content.
- Multimodal Context: Optionally include images as part of the input, enabling use with advanced multimodal models.
- Customizable Behavior: Use the system message to control model tone, persona, or instructions.
- Workflow Integration: Designed to fit seamlessly into larger AI workflows, passing outputs as messages for further action.
Typical Use Cases
- Conversational AI (chatbots, assistants)
- Automated content or email generation
- Summarization or paraphrasing tasks
- AI-powered data enrichment
- Multimodal interpretation (if images provided and supported by the model)
Summary Table
Feature | Supported? |
---|
Text input | Yes |
Image input | Optional |
LLM selection | Yes |
System instructions | Optional |
Text output | Yes |
The Generator component is a foundational building block for workflows that require dynamic, AI-generated text, offering flexibility and configurability for a wide range of applications.