Generator
The Generator component uses a selected LLM model to generate text based on prompts, optional system messages, and images—ideal for creating dynamic chatbot replies.
Component description
How the Generator component works
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.
Inputs
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.
Examples of flow templates using Generator component
To help you get started quickly, we have prepared several example flow templates that demonstrate how to use the Generator component effectively. These templates showcase different use cases and best practices, making it easier for you to understand and implement the component in your own projects.
Frequently asked questions
- What does the Generator component do?
The Generator component creates text by sending a user prompt, optional system message, and images to a chosen large language model (LLM), returning a dynamic AI-generated message.
- Can I use different LLM models with the Generator?
Yes, you can select from available LLM models to power your text generation, allowing flexibility and customization in your workflow.
- Is it possible to provide images as input to the Generator?
Yes, you can include images to enhance the input context for the model, enabling richer and more informed AI responses.
- Do I need to set a system message for every prompt?
No, system messages are optional and can be used to guide the model’s behavior or tone when needed.
- How does the Generator fit into chatbot workflows?
It acts as the core logic engine, transforming user inputs and contextual data into intelligent, AI-generated replies within your chatbot or automation flow.
Try FlowHunt Generator
Start building smarter chatbots and automation flows with the Generator component—harness the power of advanced AI text generation.
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