> ## Documentation Index
> Fetch the complete documentation index at: https://docs.embedchain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 🔭 OpenLIT

> OpenTelemetry-native Observability and Evals for LLMs & GPUs

Embedchain now supports integration with [OpenLIT](https://github.com/openlit/openlit).

## Getting Started

### 1. Set environment variables

```bash theme={null}
# Setting environment variable for OpenTelemetry destination and authetication.
export OTEL_EXPORTER_OTLP_ENDPOINT = "YOUR_OTEL_ENDPOINT"
export OTEL_EXPORTER_OTLP_HEADERS = "YOUR_OTEL_ENDPOINT_AUTH"
```

### 2. Install the OpenLIT SDK

Open your terminal and run:

```shell theme={null}
pip install openlit
```

### 3. Setup Your Application for Monitoring

Now create an app using Embedchain and initialize OpenTelemetry monitoring

```python theme={null}
from embedchain import App
import OpenLIT

# Initialize OpenLIT Auto Instrumentation for monitoring.
openlit.init()

# Initialize EmbedChain application.
app = App()

# Add data to your app
app.add("https://en.wikipedia.org/wiki/Elon_Musk")

# Query your app
app.query("How many companies did Elon found?")
```

### 4. Visualize

Once you've set up data collection with OpenLIT, you can visualize and analyze this information to better understand your application's performance:

* **Using OpenLIT UI:** Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.

* **Integrate with existing Observability Tools:** If you use tools like Grafana or DataDog, you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).