In this guide, we will show how to send users’ interactions with an OpenAI model to Nebuly’s platform. Before getting started, you would need two API keys:
  • Your OpenAI key
  • Nebuly authentication key
import { NebulySdk } from "@nebuly-ai/nebuly-js-sdk"
import { OpenAI } from "openai";

const openai = new OpenAI({
apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

async function main() {
    const nebulySdk = new NebulySdk('NEBULY_API_KEY');
    const modelInputs: OpenAI.Chat.ChatCompletionCreateParams = {
        messages: [{ role: 'user', content: 'Say this is a test' }],
        model: 'gpt-3.5-turbo',
    }
    const startTime = new Date();
    const chatCompletion = await openai.chat.completions.create(modelInputs);
    const endTime = new Date();
    
    nebulySdk.sendOpenAIInteraction(
        modelInputs['messages'],
        chatCompletion.choices[0].message.content as string,
        modelInputs['model'] as string,
        startTime,
        endTime,
        '<YOUR_USER_ID>'
    );
}

main();
Below you can find a detailed explanation of the parameters:
​
input
string
required
Input prompt from the user
​
output
string
required
Output from the LLM model
​
time_start
string
required
When the end-user request process started
​
time_end
string
required
When the end-user request process ended
​
end_user
string
required
An id or username uniquely identifying the end-user. We recommend hashing their username or email address, in order to avoid sending us any identifying information.
​
model
string
required
The LLM model you are using. Please note that this is needed if you want to visualize the cost of your requests. Now we support costs only for OpenAI models, cost of other providers coming soon.
​
history
array
History is an array of tuples of input and output, representing the entire conversation. It should be structured as the example below:
history = [
    ["input_1", "output_1"],
    ["input_2", "output_2"]
]
In this format, input_* and output_* stand for the input and output of each interaction. So, input_1 and output_1 are the input and output of the first interaction, input_2 and output_2 are for the second interaction, and so on.
​
tags
object
Tag user interactions by adding key-value pairs using this parameter. Each key represents the tag name, and the corresponding value is the tag value.For example, if you want to tag an interaction with the model version used to reply to user input, provide it as an argument for tags, e.g. {version: "v1.0.0"}. You have the flexibility to define custom tags, making them available as potential filters on the Nebuly platform.
You can find all the API details in the API documentation.