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Chatbot Concepts

PySpur allows you to create two types of Spurs: standard workflows and chatbots. This guide explains what chatbots are in PySpur, how they differ from standard workflows, and why you might want to use them.

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What Are Chatbots in PySpur?

In PySpur, a chatbot is a special type of workflow designed to handle conversational interactions. Unlike standard workflows that process data in a one-time execution flow, chatbots:

• Maintain conversation history across multiple interactions
• Process user messages and generate assistant responses
• Handle user sessions to keep conversations separate
• Support conversational context and state management

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How Chatbots Differ from Standard Workflows

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Required Input/Output Fields

Chatbots in PySpur have a predefined structure to support conversations:

Required Input Fields:

• user_message (string): The message from the user
• session_id (string): A unique identifier for the conversation session
• message_history (array): Previous messages in the conversation (automatically managed)

Required Output Fields:

• assistant_message (string): The response message from the chatbot

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When to Use Chatbots

Choose a chatbot Spur when you need to:

• Create conversational interfaces for your users
• Build customer support or information retrieval systems
• Develop virtual assistants that remember context
• Design interactive Q&A systems

Choose a standard workflow when you need to:

• Process data in a one-time operation
• Build automation pipelines without conversation
• Create custom data transformations with flexible inputs/outputs

In the next section, we’ll walk through how to create and configure a chatbot in PySpur.