Quickstart
Quickly start monitoring your LLM Applications in just a single line of code
This guide will walk you through setting up OpenTelemetry Auto Instrumentation for monitoring your LLM Application using OpenLIT. In just a few steps, you’ll be able to track and analyze the performance and usage of your LLM Applications. In this guide, we’ll show how you can send OpenTelemetry traces and metrics from your LLM Applications to Grafana Cloud
Deploy OpenLIT UI
Git Clone OpenLIT Repository
git clone [email protected]:openlit/openlit.git
Start Docker Compose
From the root directory of the OpenLIT Repo, Run the below command:
docker compose up -d
Install OpenLIT SDK
pip install openlit
Initialize OpenLIT in Your Application
Add the following two lines to your application code:
import openlit
openlit.init(otlp_endpoint="http://127.0.0.1:4318")
Example Usage for monitoring OpenAI Usage:
from openai import OpenAI
import openlit
openlit.init(otlp_endpoint="http://127.0.0.1:4318")
client = OpenAI(
api_key="YOUR_OPENAI_KEY"
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "What is LLM Observability",
}
],
model="gpt-3.5-turbo",
)
Refer to OpenLIT Python SDK repository or Typescript SDK repository for more advanced configurations and use cases.
Visualize and Analyze
With the LLM Observability data now being collected and sent to OpenLIT, the next step is to visualize and analyze this data to get insights into your LLM application’s performance, behavior, and identify areas of improvement.
Just head over to OpenLIT UI at 127.0.0.1:3000 on your browser to start exploring. You can login using the default credentials
- Email:
[email protected] - Password:
openlituser
You’re all set! Following these steps should have you on your way to effectively monitoring your LLM applications with OpenTelemetry. If you wish to send telemetry to any other backend, Refer to our Connections.
If you have any questions or need support, reach out to our community.
Was this page helpful?