Address PR review comments: rename Azure AI Foundry to Microsoft Foundry, fix redis_history_provider.py consistency, clean up expenses.csv

pamelafox · pamelafox · commit 3afb96fd2211 · 2026-04-06T09:33:51.000-07:00
diff --git a/.env.sample b/.env.sample
@@ -18,5 +18,5 @@ APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=YOUR-KEY;IngestionEndpo
 # 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
+# Optional: Set to log evaluation results to Microsoft Foundry for rich visualization
 AZURE_AI_PROJECT=https://YOUR-ACCOUNT.services.ai.azure.com/api/projects/YOUR-PROJECT
diff --git a/README.md b/README.md
@@ -17,14 +17,14 @@ urlFragment: python-agentframework-demos
 [![Open in GitHub Codespaces](https://img.shields.io/static/v1?style=for-the-badge&label=GitHub+Codespaces&message=Open&color=brightgreen&logo=github)](https://codespaces.new/Azure-Samples/python-agentframework-demos)
 [![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/Azure-Samples/python-agentframework-demos)
 
-This repository provides examples of [Microsoft Agent Framework](https://learn.microsoft.com/agent-framework/) using LLMs from [Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/) or other model providers.
+This repository provides examples of [Microsoft Agent Framework](https://learn.microsoft.com/agent-framework/) using LLMs from [Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/) or other model providers.
 
 * [Getting started](#getting-started)
   * [GitHub Codespaces](#github-codespaces)
   * [VS Code Dev Containers](#vs-code-dev-containers)
   * [Local environment](#local-environment)
 * [Configuring model providers](#configuring-model-providers)
-  * [Using Azure AI Foundry models](#using-azure-ai-foundry-models)
+  * [Using Microsoft Foundry models](#using-microsoft-foundry-models)
   * [Using OpenAI.com models](#using-openaicom-models)
 * [Running the Python examples](#running-the-python-examples)
 * [Resources](#resources)
@@ -94,11 +94,11 @@ The dev container includes a Redis server, which is used by the `agent_history_r
 
 ## Configuring model providers
 
-These examples can be run with Azure AI Foundry or OpenAI.com, depending on the environment variables you set. All the scripts reference the environment variables from a `.env` file, and an example `.env.sample` file is provided. Host-specific instructions are below.
+These examples can be run with Microsoft Foundry or OpenAI.com, depending on the environment variables you set. All the scripts reference the environment variables from a `.env` file, and an example `.env.sample` file is provided. Host-specific instructions are below.
 
-## Using Azure AI Foundry models
+## Using Microsoft Foundry models
 
-This project includes infrastructure as code (IaC) to provision Azure OpenAI deployments of "gpt-5.4" and "text-embedding-3-large" via Azure AI Foundry. The IaC is defined in the `infra` directory and uses the Azure Developer CLI to provision the resources.
+This project includes infrastructure as code (IaC) to provision Azure OpenAI deployments of "gpt-5.4" and "text-embedding-3-large" via Microsoft Foundry. The IaC is defined in the `infra` directory and uses the Azure Developer CLI to provision the resources.
 
 1. Make sure the [Azure Developer CLI (azd)](https://aka.ms/install-azd) is installed.
 
@@ -208,7 +208,7 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [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_otel_appinsights.py](examples/agent_otel_appinsights.py) | An agent with OpenTelemetry tracing, metrics, and structured logs exported to [Azure Application Insights](https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview). Requires Azure provisioning via `azd provision`. |
 | [agent_evaluation_generate.py](examples/agent_evaluation_generate.py) | Generate synthetic evaluation data for the travel planner agent. |
-| [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). |
+| [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 [Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk). |
 | [agent_evaluation_batch.py](examples/agent_evaluation_batch.py) | Batch evaluation of agent responses using Azure AI Evaluation's `evaluate()` function. |
 | [agent_redteam.py](examples/agent_redteam.py) | Red-team a financial advisor agent using [Azure AI Evaluation](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/red-teaming-agent) to test resilience against adversarial attacks across risk categories (Violence, HateUnfairness, Sexual, SelfHarm). Requires `AZURE_AI_PROJECT` in `.env`. |
 
@@ -279,7 +279,7 @@ This example requires an `APPLICATIONINSIGHTS_CONNECTION_STRING` environment var
 
 **Option A: Automatic via `azd provision`**
 
-If you run `azd provision` (see [Using Azure AI Foundry models](#using-azure-ai-foundry-models)), the Application Insights resource is provisioned automatically and the connection string is written to your `.env` file.
+If you run `azd provision` (see [Using Microsoft Foundry models](#using-microsoft-foundry-models)), the Application Insights resource is provisioned automatically and the connection string is written to your `.env` file.
 
 **Option B: Manual from the Azure Portal**
 
diff --git a/examples/agent_evaluation_batch.py b/examples/agent_evaluation_batch.py
@@ -1,12 +1,12 @@
 """Batch evaluation of agent responses using Azure AI Evaluation's evaluate() function.
 
 Reads evaluation data from a JSONL file (produced by agent_evaluation_generate.py) and runs
-all evaluators in a single batch call. Optionally logs results to Azure AI Foundry
+all evaluators in a single batch call. Optionally logs results to Microsoft Foundry
 if AZURE_AI_PROJECT is set.
 
 Usage:
     python agent_evaluation_batch.py                          # uses eval_data.jsonl
-    AZURE_AI_PROJECT=<url> python agent_evaluation_batch.py   # logs to Azure AI Foundry
+    AZURE_AI_PROJECT=<url> python agent_evaluation_batch.py   # logs to Microsoft Foundry
 """
 
 import logging
@@ -48,7 +48,7 @@
         model=os.environ.get("OPENAI_MODEL", "gpt-5.4"),
     )
 
-# Optional: Set AZURE_AI_PROJECT in .env to log results to Azure AI Foundry.
+# Optional: Set AZURE_AI_PROJECT in .env to log results to Microsoft Foundry.
 # Example: https://your-account.services.ai.azure.com/api/projects/your-project
 AZURE_AI_PROJECT = os.getenv("AZURE_AI_PROJECT")
 
@@ -134,7 +134,7 @@ def main() -> None:
     if AZURE_AI_PROJECT:
         studio_url = eval_result.get("studio_url")
         if studio_url:
-            print(f"\nView results in Azure AI Foundry:\n{studio_url}")
+            print(f"\nView results in Microsoft Foundry:\n{studio_url}")
     else:
         logger.info("Results saved to eval_results.json")
 
diff --git a/examples/expenses.csv b/examples/expenses.csv
@@ -1,7 +1,2 @@
 date,amount,category,description,payment_method
 2026-02-08,1200.0,gadget,Laptop purchase,visa
-2026-04-02,1200.0,gadget,laptop,visa
-2026-04-02,1200.0,gadget,Compra de laptop,visa
-2026-04-02,1200.0,gadget,Laptop purchase,visa
-2026-04-02,1200.0,gadget,Compra de laptop,visa
-2026-04-02,1200.0,gadget,Compra de laptop,visa
diff --git a/examples/redis_history_provider.py b/examples/redis_history_provider.py
@@ -1,5 +1,13 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+"""
+Redis History Provider Session Example
+
+This sample demonstrates how to use Redis as a history provider for session
+management, enabling persistent conversation history storage across sessions
+with Redis as the backend data store.
+"""
+
 import asyncio
 import os
 from uuid import uuid4
@@ -11,28 +19,27 @@
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
-load_dotenv()
-
-"""
-Redis History Provider Session Example
-
-This sample demonstrates how to use Redis as a history provider for session
-management, enabling persistent conversation history storage across sessions
-with Redis as the backend data store.
-"""
+load_dotenv(override=True)
+API_HOST = os.getenv("API_HOST", "azure")
 
 # Default Redis URL for local Redis Stack.
 # Override via the REDIS_URL environment variable for remote or authenticated instances.
 REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
 
 
-async_credential = DefaultAzureCredential()
-token_provider = get_bearer_token_provider(async_credential, "https://cognitiveservices.azure.com/.default")
-client = OpenAIChatClient(
-    base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
-    api_key=token_provider,
-    model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
-)
+async_credential = None
+if API_HOST == "azure":
+    async_credential = DefaultAzureCredential()
+    token_provider = get_bearer_token_provider(async_credential, "https://cognitiveservices.azure.com/.default")
+    client = OpenAIChatClient(
+        base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
+        api_key=token_provider,
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+    )
+else:
+    client = OpenAIChatClient(
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-5.4")
+    )
 
 
 async def example_manual_memory_store() -> None:
@@ -268,6 +275,9 @@ async def main() -> None:
     except Exception as e:
         print(f"Error running examples: {e}")
         raise
+    finally:
+        if async_credential:
+            await async_credential.close()
 
 
 if __name__ == "__main__":
diff --git a/examples/spanish/README.md b/examples/spanish/README.md
@@ -1,7 +1,7 @@
 <!--
 ---
 name: Python Agent Framework Demos
-description: Colección de ejemplos en Python para Microsoft Agent Framework usando Azure AI Foundry.
+description: Colección de ejemplos en Python para Microsoft Agent Framework usando Microsoft Foundry.
 languages:
 - python
 products:
@@ -18,14 +18,14 @@ urlFragment: python-agentframework-demos
 [![Abrir en GitHub Codespaces](https://img.shields.io/static/v1?style=for-the-badge&label=GitHub+Codespaces&message=Open&color=brightgreen&logo=github)](https://codespaces.new/Azure-Samples/python-agentframework-demos)
 [![Abrir en Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/Azure-Samples/python-agentframework-demos)
 
-Este repositorio ofrece ejemplos de [Microsoft Agent Framework](https://learn.microsoft.com/agent-framework/) usando LLMs de [Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/) u otros proveedores de modelos.
+Este repositorio ofrece ejemplos de [Microsoft Agent Framework](https://learn.microsoft.com/agent-framework/) usando LLMs de [Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/) u otros proveedores de modelos.
 
 * [Cómo empezar](#cómo-empezar)
   * [GitHub Codespaces](#github-codespaces)
   * [VS Code Dev Containers](#vs-code-dev-containers)
   * [Entorno local](#entorno-local)
 * [Configurar proveedores de modelos](#configurar-proveedores-de-modelos)
-  * [Usar modelos de Azure AI Foundry](#usar-modelos-de-azure-ai-foundry)
+  * [Usar modelos de Microsoft Foundry](#usar-modelos-de-microsoft-foundry)
   * [Usar modelos de OpenAI.com](#usar-modelos-de-openaicom)
 * [Ejecutar los ejemplos en Python](#ejecutar-los-ejemplos-en-python)
 * [Recursos](#recursos)
@@ -95,11 +95,11 @@ El dev container incluye un servidor Redis, que se usa en el ejemplo `agent_hist
 
 ## Configurar proveedores de modelos
 
-Estos ejemplos se pueden ejecutar con Azure AI Foundry u OpenAI.com, dependiendo de las variables de entorno que configures. Todos los scripts hacen referencia a las variables de entorno de un archivo `.env`, y se proporciona un archivo de ejemplo `.env.sample`. Las instrucciones específicas de cada proveedor se encuentran a continuación.
+Estos ejemplos se pueden ejecutar con Microsoft Foundry u OpenAI.com, dependiendo de las variables de entorno que configures. Todos los scripts hacen referencia a las variables de entorno de un archivo `.env`, y se proporciona un archivo de ejemplo `.env.sample`. Las instrucciones específicas de cada proveedor se encuentran a continuación.
 
-## Usar modelos de Azure AI Foundry
+## Usar modelos de Microsoft Foundry
 
-Este proyecto incluye infraestructura como código (IaC) para provisionar despliegues de Azure OpenAI de "gpt-5.4" y "text-embedding-3-large" a través de Azure AI Foundry. La IaC está definida en el directorio `infra` y usa Azure Developer CLI para provisionar los recursos.
+Este proyecto incluye infraestructura como código (IaC) para provisionar despliegues de Azure OpenAI de "gpt-5.4" y "text-embedding-3-large" a través de Microsoft Foundry. La IaC está definida en el directorio `infra` y usa Azure Developer CLI para provisionar los recursos.
 
 1. Asegúrate de tener instalado [Azure Developer CLI (azd)](https://aka.ms/install-azd).
 
@@ -203,7 +203,7 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [agent_otel_aspire.py](agent_otel_aspire.py) | Un agente con trazas, métricas y logs estructurados de OpenTelemetry exportados al [Aspire Dashboard](https://aspire.dev/dashboard/standalone/). |
 | [agent_otel_appinsights.py](agent_otel_appinsights.py) | Un agente con trazas, métricas y logs estructurados de OpenTelemetry exportados a [Azure Application Insights](https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview). Requiere aprovisionamiento de Azure con `azd provision`. |
 | [agent_evaluation_generate.py](agent_evaluation_generate.py) | Genera datos sintéticos de evaluación para el agente planificador de viajes. |
-| [agent_evaluation.py](agent_evaluation.py) | Evalúa un agente planificador de viajes usando evaluadores de [Azure AI Evaluation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/agent-evaluators) (IntentResolution, ToolCallAccuracy, TaskAdherence, ResponseCompleteness). Opcionalmente configura `AZURE_AI_PROJECT` en `.env` para registrar resultados en [Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk). |
+| [agent_evaluation.py](agent_evaluation.py) | Evalúa un agente planificador de viajes usando evaluadores de [Azure AI Evaluation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/agent-evaluators) (IntentResolution, ToolCallAccuracy, TaskAdherence, ResponseCompleteness). Opcionalmente configura `AZURE_AI_PROJECT` en `.env` para registrar resultados en [Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk). |
 | [agent_evaluation_batch.py](agent_evaluation_batch.py) | Evaluación por lotes de respuestas de agentes con la función `evaluate()` de Azure AI Evaluation. |
 | [agent_redteam.py](agent_redteam.py) | Prueba de red team a un agente asesor financiero usando [Azure AI Evaluation](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/red-teaming-agent) para evaluar su resiliencia ante ataques adversariales en categorías de riesgo (Violence, HateUnfairness, Sexual, SelfHarm). Requiere `AZURE_AI_PROJECT` en `.env`. |
 
@@ -274,7 +274,7 @@ Este ejemplo requiere la variable de entorno `APPLICATIONINSIGHTS_CONNECTION_STR
 
 **Opción A: Automática con `azd provision`**
 
-Si ejecutas `azd provision` (consulta [Usar modelos de Azure AI Foundry](#usar-modelos-de-azure-ai-foundry)), el recurso de Application Insights se provisiona automáticamente y la cadena de conexión se escribe en tu archivo `.env`.
+Si ejecutas `azd provision` (consulta [Usar modelos de Microsoft Foundry](#usar-modelos-de-microsoft-foundry)), el recurso de Application Insights se provisiona automáticamente y la cadena de conexión se escribe en tu archivo `.env`.
 
 **Opción B: Manual desde el Portal de Azure**
 
diff --git a/examples/spanish/agent_evaluation_batch.py b/examples/spanish/agent_evaluation_batch.py
@@ -2,11 +2,11 @@
 
 Lee datos de evaluación de un archivo JSONL (producido por agent_evaluation_generate.py) y ejecuta
 todos los evaluadores en una sola llamada por lotes. Opcionalmente registra resultados en
-Azure AI Foundry si AZURE_AI_PROJECT está configurado.
+Microsoft Foundry si AZURE_AI_PROJECT está configurado.
 
 Uso:
     python agent_evaluation_batch.py                          # usa eval_data.jsonl
-    AZURE_AI_PROJECT=<url> python agent_evaluation_batch.py   # registra en Azure AI Foundry
+    AZURE_AI_PROJECT=<url> python agent_evaluation_batch.py   # registra en Microsoft Foundry
 """
 
 import logging
@@ -48,7 +48,7 @@
         model=os.environ.get("OPENAI_MODEL", "gpt-5.4"),
     )
 
-# Opcional: Establece AZURE_AI_PROJECT en .env para registrar resultados en Azure AI Foundry.
+# Opcional: Establece AZURE_AI_PROJECT en .env para registrar resultados en Microsoft Foundry.
 # Ejemplo: https://tu-cuenta.services.ai.azure.com/api/projects/tu-proyecto
 AZURE_AI_PROJECT = os.getenv("AZURE_AI_PROJECT")
 
@@ -134,7 +134,7 @@ def main() -> None:
     if AZURE_AI_PROJECT:
         studio_url = eval_result.get("studio_url")
         if studio_url:
-            print(f"\nVer resultados en Azure AI Foundry:\n{studio_url}")
+            print(f"\nVer resultados en Microsoft Foundry:\n{studio_url}")
     else:
         logger.info("Resultados guardados en eval_results.json")
 
diff --git a/presentations/english/session-2/README.md b/presentations/english/session-2/README.md
diff --git a/presentations/english/session-3/README.md b/presentations/english/session-3/README.md
