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

# Embeddings

> Monitor OpenAI Embeddings API usage

## Create Embeddings

**Python**

```python
from openai import AzureOpenAI
import dokumetry

client = AzureOpenAI(
  api_key = "YOUR_AZURE_OPENAI_API_KEY",  
  api_version = "2024-02-01",
  azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)

azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"

# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")

client.embeddings.create(
  model=azure_model_deployment,
  input="LLM monitoring",
  encoding_format="float"
)
```

**javascript**

```javascript
import OpenAI from "openai";
import DokuMetry from 'dokumetry';

const client = new OpenAI({
  apiKey: "YOUR_AZURE_OPENAI_API_KEY",
  baseURL: `https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/openai/deployments/YOUR_AZURE_OPENAI_DEPLOYMENT`,
  defaultQuery: { 'api-version': "2024-02-01" },
  defaultHeaders: { 'api-key': "YOUR_AZURE_OPENAI_API_KEY" },
});

const azureModelDeployment = 'YOUR_AZURE_OPENAI_DEPLOYMENT';

# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")

async function main() {
  const embedding = await client.embeddings.create({
    model: azureModelDeployment,
    input: "The quick brown fox jumped over the lazy dog",
    encoding_format: "float",
  });

  console.log(embedding);
}

main();
```


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