Qwen Chat Prompt and Response
The Qwen Chat Prompt Example Flow demonstrates how to interact with the Qwen Chat API to send prompts and retrieve AI-generated responses. This flow covers the entire process from sending user-defined prompts to receiving, formatting, and storing the responses as structured records. It includes robust error handling and testing capabilities, making it an ideal starting point for integrating AI-driven chat functionality into your applications.
You can find this template in the Services Catalog under these categories:
AI, Contextual Basics
What's Included:
1 Flow: Pre-configured for prompt-response interactions with Qwen Chat.
1 Object Type: To store structured prompt-response records.
1 Connection Template: For integrating with the Qwen Chat API.
What You'll Need:
Access to the Qwen Chat API.
API Key or credentials for authentication.
Sample prompts or use cases for testing.
Ideas for Using the Qwen Chat Prompt Flow:
Conversational AI: Automate chat-based interactions for virtual assistants or customer support bots.
Content Creation: Generate creative content or responses based on tailored prompts.
Decision Support: Process complex queries to retrieve insights for decision-making.
Education: Leverage AI-generated explanations for teaching or self-learning purposes.
Flow Overview
Flow Start
Node:
contextual-start
Purpose: Triggers the flow, initiated by an external event, agent, or the Inject node for testing.
In-Editor Testing
Nodes:
Test Prompt
,Prepare Prompt
Purpose: Enables in-editor testing with a sample prompt. Modify the provided sample prompt, such as "How many 'r's are in Strawberry?" to explore different interactions with the Qwen Chat API.
Code Example: Prepare Prompt Function
Send Prompt and Receive Response
Nodes:
Prompt Qwen Chat
,Qwen Chat Response
Purpose: Sends the prepared payload to the Qwen Chat API. Logs the response for review and passes it for further processing.
Code Example: Response Logging
Format Response & Create Record
Nodes:
Prepare Record Data
,Create AI Response Record
Purpose: Formats the API response and creates a structured record containing the original prompt and the AI-generated response.
Code Example: Prepare Record Data
Error Handling
Nodes:
catch
,Error Catch Log
,contextual-error
Purpose: Captures and logs any errors during the flow execution, ensuring efficient debugging and troubleshooting.
Flow End
Node:
contextual-end
Purpose: Concludes the flow after records are successfully created or errors are logged.
Summary of Flow Steps:
Start: Trigger the flow manually or externally.
Data Preparation: Format the prompt for API interaction.
API Interaction: Submit the prompt and process the AI response.
Record Creation: Structure and store the prompt-response data.
Error Handling: Log and manage any issues that arise.
Completion: Conclude the flow.
Key Features:
Flexibility: Easily adaptable to various use cases.
Scalability: Integrates seamlessly into broader workflows.
Customization: Modify prompts, response handling, and storage as needed.
This flow serves as a robust template for leveraging AI-powered chat functionality, providing clear examples and extensibility for diverse applications.
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