.example.env

# ---------- Azure OpenAI ----------
# Your Azure OpenAI resource endpoint (include trailing slash)
AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com/
# Your Azure OpenAI API key
AZURE_OPENAI_API_KEY=<your-azure-openai-key>
# The API version compatible with your deployment
AZURE_OPENAI_API_VERSION=2024-10-21
# Your deployment name (e.g., gpt-4o-mini, gpt-4o-mini-2024-07-18, etc.)
AZURE_OPENAI_DEPLOYMENT=gpt-4o-mini

# ---------- Datadog LLM Observability (Agentless, optional) ----------
# Your Datadog API key
DD_API_KEY=<your-datadog-api-key>
# Your Datadog site (e.g., datadoghq.com, us3.datadoghq.com, datadoghq.eu)
DD_SITE=datadoghq.com
# Application name to group traces/metrics in Datadog
DD_LLMOBS_ML_APP=quickstart-app

# ---------- Notes ----------
# 1) Copy this file to `.env` and fill in the placeholders.
# 2) The notebook auto-loads `.env` if present (python-dotenv).
# 3) To launch with ddtrace-run instead of embedded init:
#    DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=$DD_LLMOBS_ML_APP DD_API_KEY=$DD_API_KEY ddtrace-run jupyter lab

langgraph_azure_openai_mcp_minimal.ipynb