Merge main into context-examples: resolve conflicts in .env.sample, pyproject.toml, uv.lock

Author: pamelafox

.env.sample

@@ -18,3 +18,5 @@ OTEL_EXPORTER_OTLP_PROTOCOL=grpc
 # Configure for Azure AI Search (used by agent_knowledge_aisearch.py):
 AZURE_SEARCH_ENDPOINT=https://YOUR-SEARCH-SERVICE.search.windows.net
 AZURE_SEARCH_KNOWLEDGE_BASE_NAME=YOUR-KB-NAME
+# Optional: Set to log evaluation results to Azure AI Foundry for rich visualization
+AZURE_AI_PROJECT=https://YOUR-ACCOUNT.services.ai.azure.com/api/projects/YOUR-PROJECT

README.md

@@ -192,6 +192,7 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [openai_tool_calling.py](examples/openai_tool_calling.py) | Tool calling with the low-level OpenAI SDK, showing manual tool dispatch. |
 | [workflow_basic.py](examples/workflow_basic.py) | A workflow-based agent. |
 | [agent_otel_aspire.py](examples/agent_otel_aspire.py) | An agent with OpenTelemetry tracing, metrics, and structured logs exported to the [Aspire Dashboard](https://aspire.dev/dashboard/standalone/). |
+| [agent_evaluation.py](examples/agent_evaluation.py) | Evaluate a travel planner agent using [Azure AI Evaluation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/agent-evaluators) agent evaluators (IntentResolution, ToolCallAccuracy, TaskAdherence, ResponseCompleteness). Optionally set `AZURE_AI_PROJECT` in `.env` to log results to [Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk). |
 
 ## Using the Aspire Dashboard for telemetry