via Tracing

KEYWORDSAI_BASE_URL="https://api.keywordsai.co/api"
KEYWORDSAI_API_KEY="YOUR_KEYWORDSAI_API_KEY"

from keywordsai_tracing.decorators import workflow, task
from keywordsai_tracing.main import KeywordsAITelemetry

k_tl = KeywordsAITelemetry()

@workflow(name="my_workflow")
def my_workflow():
    @task(name="my_task")
    def my_task():
        pass
    my_task()

from openai import OpenAI
from keywordsai_tracing.decorators import workflow, task
from keywordsai_tracing.main import KeywordsAITelemetry

k_tl = KeywordsAITelemetry()
client = OpenAI()

@workflow(name="create_joke")
def create_joke():
    completion = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Tell me a joke about opentelemetry"}],
        temperature=0.5,
        max_tokens=100,
        frequency_penalty=0.5,
        presence_penalty=0.5,
    )
    return completion.choices[0].message.content

npm install @keywordsai/tracing
# or yarn

yarn add @keywordsai/tracing

KEYWORDSAI_BASE_URL="https://api.keywordsai.co/api"
KEYWORDSAI_API_KEY="YOUR_KEYWORDSAI_API_KEY"

import { KeywordsAITelemetry } from '@keywordsai/tracing';
import OpenAI from 'openai';

// Initialize clients
// Make sure to set these environment variables or pass them directly
const keywordsAi = new KeywordsAITelemetry({
    apiKey: process.env.KEYWORDSAI_API_KEY || "",
    appName: 'test-app',
    disableBatch: true  // For testing, disable batching
});

const openai = new OpenAI();

// This demonstrates a simple LLM call wrapped in a task
async function createJoke() {
    return await keywordsAi.withTask(
        { name: 'joke_creation' },
        async () => {
            const completion = await openai.chat.completions.create({
                messages: [{ role: 'user', content: 'Tell me a joke about TypeScript' }],
                model: 'gpt-4o-mini',
                temperature: 0.7
            });
            return completion.choices[0].message.content;
        }
    );
}

async function jokeWorkflow() {
    return await keywordsAi.withWorkflow(
        { name: 'pirate_joke_workflow' },
        async () => {
            const joke = await createJoke();
            const pirateJoke = await translateJoke(joke);
            return pirateJoke;
        }
    );
}