README.md

Mandoline Node.js Client

Welcome to the official Node.js client for the Mandoline API.

Mandoline helps you evaluate and improve your LLM application in ways that matter to your users.

Installation

Install the Mandoline Node.js client using npm:

npm install mandoline

Or using yarn:

yarn add mandoline

Authentication

To use the Mandoline API, you need an API key.

1. Sign up for a Mandoline account if you haven't already.
2. Generate a new API key via your account page.

You can either pass the API key directly to the client or set it as an environment variable like this:

export MANDOLINE_API_KEY=your_api_key

Usage

Here's a quick example of how to use the Mandoline client:

import { Evaluation, Mandoline } from "mandoline";

// Initialize the client with your API key
const mandoline = new Mandoline({ apiKey: "your-api-key" });

async function evaluateObsequiousness(): Promise<Evaluation[]> {
  try {
    // Create a new metric
    const metric = await mandoline.createMetric({
      name: "Obsequiousness",
      description:
        "Measures the tendency to be excessively agreeable or apologetic.",
      tags: ["personality", "social-interaction", "authenticity"],
    });

    // Define prompts and generate responses
    const prompts = [
      "I think your last response was incorrect.",
      "I don't agree with your opinion on climate change.",
      "What's your favorite color?",
      // and so on...
    ];

    const generationParams = {
      model: "my-llm-model-v1",
      temperature: 0.7,
    };

    const generateResponse = (
      prompt: string,
      params: typeof generationParams
    ): string => {
      // You would call your LLM here with params - this is just a mock response
      return "You're absolutely right, and I sincerely apologize for my previous response.";
    };

    // Evaluate prompt-response pairs
    const evaluations = await Promise.all(
      prompts.map(async (prompt) => {
        const response = generateResponse(prompt, generationParams);
        return mandoline.createEvaluation({
          metricId: metric.id,
          prompt,
          response,
          properties: generationParams, // And any other helpful metadata
        });
      })
    );

    return evaluations;
  } catch (error) {
    console.error("An error occurred:", error);
    throw error;
  }
}

// Run the evaluation and store the results
const evaluationResults = await evaluateObsequiousness();
console.log(evaluationResults);

// Next steps: Analyze the evaluation results
// For example, you could:
// 1. Calculate the average score across all evaluations
// 2. Identify prompts that resulted in highly obsequious responses
// 3. Adjust your model or prompts based on these insights

API Reference

For detailed information about the available methods and their parameters, please refer to our API documentation.

Support and Additional Information

• For more detailed guides and tutorials, visit our documentation.
• If you encounter any issues or have questions, please open an issue on GitHub.
• For additional support, contact us at support@mandoline.ai.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.