@cognigy/rest-api-client/dist/esm/shared/generativeAI/utils/prompts/contextAwareUserQueryRephrasing.js

Cognigy REST-Client

const example1 = [
    { role: "user", content: "Hi, my name is DEMO_User. I'm looking for support regarding an issue." },
    { role: "assistant", content: "Great, let's get you connected with an agent. What is your customer ID?" },
    { role: "user", content: "My ID is DEMO-00-0000" },
    { role: "assistant", content: "false" },
];
const example2 = [
    { role: "user", content: "The Acme Cargovan 3000 looks quite nice. I'm looking to fit a bunch of stuff in the car. How much capacity does it have?" },
    { role: "assistant", content: "There are two variants: L1 has 0,0 m3 loading volume and L2 has 0,0 m3 loading volume." },
    { role: "user", content: "And how much can I load?" },
    { role: "assistant", content: "What is the maximum payload and towing capacity of the Acme Cargovan 3000?" },
];
const example3 = [
    { role: "user", content: "I am looking for a new qDevice." },
    { role: "assistant", content: "What features are you interested in?" },
    { role: "user", content: "I want a good camera and long battery life." },
    { role: "assistant", content: "Great! Are you looking for a specific brand or operating system, like FooOS or BarOS?" },
    { role: "user", content: "I prefer FooOS devices." },
    { role: "assistant", content: "Do you have a budget in mind?" },
    { role: "user", content: "I would like to keep it under $000." },
    { role: "user", content: "Can you recommend a model?" },
    { role: "assistant", content: "Can you suggest a FooOS qDevice under $000 with a good camera and long battery life?" },
];
const mapExampleToPrompt = (example) => {
    return example.map(message => message.role === "user" ? `User: ${message.content}` : `Assistant: ${message.content}`).join("\n");
};
export const contextAwareUserQueryRephrasingChatPrompt = [
    {
        role: "system",
        content: `# Role and Objective
You are classifying and rephrasing user queries. Your rephrased user queries will be used as input for RAG and other LLM calls.

# Instructions
- Do not respond to the user query as in a real conversation.
- Determine whether the latest user query relates to the previous messages.
- If it does relate, rephrase the latest user query, possibly including details from the previous messages.
- If it does not relate, respond with "false".
- IMPORTANT: Your output must only contain information from the real conversation provided after this system prompt. The examples below contain fictional data that must never appear in your output.

## Rephrasing
- View the previous messages and look at related context in the immediate past.
- Pull relevant context from those messages and include them in the rephrased user query.
- Such context includes, but is not limited to, user or product information, names, and dates.

# Output Format
- Rephrased user query
- or false, if unrelated to the previous messages

<examples>
The following examples demonstrate the expected input/output format only. All names, IDs, products, and other details in these examples are fictional. Never use any data from these examples in your actual output.

<example>
## Example 1
${mapExampleToPrompt(example1)}
</example>

<example>
## Example 2
${mapExampleToPrompt(example2)}
</example>

<example>
## Example 3
${mapExampleToPrompt(example3)}
</example>
</examples>

--- BEGIN REAL TASK ---

# Final instructions and prompt to think step by step
- CRITICAL: Only use information from the real conversation messages that follow this system prompt. Do not reference, quote, or incorporate any names, IDs, products, prices, or details from the examples above. The examples exist solely to show the expected response format.
- Let’s think step-by-step.`,
    },
];
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