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Followup Questions Component
The Followup Questions component is designed to help users generate insightful follow-up questions based on a provided context, answer, and conversation history. This functionality is especially valuable in AI-driven workflows where deepening understanding of a topic or clarifying ambiguities is important—such as in chatbots, tutoring systems, or knowledge exploration tools.
What Does the Component Do?
This component takes an input text (typically a user’s question or statement), and, using a language model, generates a list of follow-up questions that the user should ask to gain a deeper or clearer understanding of the topic. It can leverage additional information like the current chat history, context, and previous answers to generate more precise and relevant questions.
Inputs
The component supports several input fields, some optional and some required. Here’s an overview:
| Input Name | Type | Required | Description |
|---|---|---|---|
| Input Text | String (Message) | Yes | The main text input (user query or statement) to base follow-up questions on. |
| Chat History | InMemoryChatMessageHistory | No | The conversation history, which helps the model generate better-targeted follow-up questions. |
| LLM | BaseChatModel | No | The language model to use for question generation. |
| Answer | String (Message) | No | The answer to the input text, which can help improve the relevance of the follow-up questions. |
| Context | String (Message) | No | Additional context to generate more focused questions. |
| Number of questions | Integer | Yes | Specifies how many follow-up questions to generate. Default is 5. |
| System Message | String | No | An optional system-level message to modify or steer the prompt sent to the language model. |
Outputs
- Message:
The output of this component is a message (or collection of messages) containing the generated follow-up questions.
Why Is This Useful?
- Enhance User Engagement: By suggesting relevant follow-up questions, this component helps users dig deeper into topics and discover information they might not have considered.
- Improve Conversational Flows: In chatbots or virtual assistants, it prompts users to clarify or expand on their queries, making interactions more dynamic and informative.
- Support Learning and Research: In educational or research settings, it can guide learners or researchers to ask better questions, leading to improved comprehension and critical thinking.
- Personalization: By considering chat history and context, the questions are tailored and contextualized, increasing their usefulness and precision.
Example Use Cases
- Customer Support Bots: Automatically suggest helpful follow-up questions to customers based on their previous queries and responses.
- Educational Tutors: Help students by prompting them with additional questions to ensure they understand the material.
- Knowledge Management: Guide users in knowledge bases or research environments to ask productive questions.
Summary Table
| Feature | Benefit |
|---|---|
| Context-aware | Generates more relevant questions |
| Model-agnostic | Can work with different LLMs |
| Customizable output | Control over number and style of questions |
| History integration | Takes prior conversation into account |
By integrating the Followup Questions component, you can make your AI-driven workflows more interactive, informative, and user-friendly.