DeepSeek Chat Prompt and Response

The DeepSeek Chat Prompt Example Flow demonstrates how to interact with the DeepSeek API to send prompts and retrieve AI-generated chat responses. This flow showcases a complete process, from sending user-defined prompts to receiving, formatting, and storing responses for further use or integration. It includes robust error handling and testing capabilities, making it an ideal solution for incorporating AI-driven chat functionality into various applications.

You can find this template in the Services Catalog under these categories:

• AI, Contextual Basics

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What's Included:

• 1 Flow: Pre-configured for prompt-response interactions with DeepSeek.

• 1 Object Type: To store structured prompt-response records.

• 1 Connection Template: For integrating with the DeepSeek API.

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What You'll Need:

• Access to the DeepSeek API.

• API Key or credentials for authentication.

• Sample prompts or use cases for testing.

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Ideas for Using the DeepSeek Chat Prompt Flow:

1. Conversational AI: Automate chat-based interactions for virtual assistants or customer service bots.

2. Content Generation: Create engaging content by generating responses to tailored prompts.

3. Decision Support: Process complex queries to retrieve insights for informed decision-making.

4. Education: Use AI-generated explanations for learning or teaching purposes.

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Flow Overview

Flow Start

• Node: contextual-start

• Purpose: Triggers the flow, initiated by either an external event, agent, or inject node for testing.

In-Editor Testing

• Nodes: Test Prompt, Prepare Prompt

• Purpose: Enables in-editor testing with a sample prompt. Modify the sample prompt directly to explore different interactions with the DeepSeek API.

Code Example: Prepare Prompt Function

msg.payload = {
    model: "deepseek-chat",
    messages: [
        { role: "system", content: "You are a helpful assistant" },
        { role: "user", content: msg.payload.prompt }
    ],
    max_tokens: 4096
};
return msg;

Send Prompt and Receive Response

• Nodes: Prompt DeepSeek Chat, DeepSeek Chat Response

• Purpose: Sends the prepared payload to the DeepSeek API. Logs the response for review and passes it for further processing.

Code Example: Response Logging

msg.payload.response = {
    aiResponse: msg.payload.response.choices[0].message.content
};
return msg;

Format Response & Create Record

• Nodes: Prepare Record Data, Create AI Response Record

• Purpose: Formats the API response and creates a structured record, including the original prompt and AI-generated response.

Code Example: Prepare Record Data

let body = {
    prompt: msg.payload.messages[1].content,
    aiResponse: msg.payload.response.choices[0].message.content
};
msg.payload = body;
return msg;

Error Handling

• Nodes: catch, Error Catch Log, contextual-error

• Purpose: Captures and logs any errors during the flow execution to ensure efficient debugging and troubleshooting.

Flow End

• Node: contextual-end

• Purpose: Successfully concludes the process after records are created or errors are logged.

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Summary of Flow Steps

1. Start: Trigger the flow manually or externally.

2. Data Preparation: Format the prompt for API interaction.

3. API Interaction: Submit the prompt and receive the response.

4. Record Creation: Structure and store the response.

5. Error Handling: Log and manage any issues that arise.

6. Completion: Conclude the flow after creating records or logging errors.

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Key Features

• Flexibility: Can be easily adapted to various use cases.

• Scalability: Integrates seamlessly into broader workflows.

• Customization: Modify prompts, response handling, and storage to suit specific requirements.

This flow is a robust starting point for leveraging AI-powered chat functionality within your workflows, providing clear examples and extensibility for diverse applications.