Standardize environment variable naming and fix syntax errors across chapters 01-11

[Copilot]

Inconsistent env var names (AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_CHAT_DEPLOYMENT_NAME) scattered across README code snippets conflicted with .env.example standards (AZURE_AI_PROJECT_ENDPOINT, AZURE_AI_MODEL_DEPLOYMENT_NAME). Several AzureAIProjectAgentProvider instantiations also had a systematic syntax error — a stray ,) that made the code unparseable.

Syntax fixes

Corrected broken AzureAIProjectAgentProvider calls across notebooks and READMEs (lessons 02, 04, 06, 07, 10, 12). Pattern was consistently:

# Before — SyntaxError: stray ),  followed by dangling keyword args
provider = AzureAIProjectAgentProvider(credential=AzureCliCredential()),
    project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
)

# After — framework auto-reads AZURE_AI_PROJECT_ENDPOINT from env
provider = AzureAIProjectAgentProvider(credential=AzureCliCredential())

Env var name standardization

• 04-tool-use/README.md: AZURE_OPENAI_ENDPOINT → AZURE_AI_PROJECT_ENDPOINT
• 07-planning-design/README.md: removed AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME references; provider now uses env-auto-detection
• 06-building-trustworthy-agents/README.md: removed incorrect endpoint= / model= params from provider constructor

.env.example and setup docs

Added missing variables so the root .env.example covers all lessons 01–11:

# GitHub Models (Lessons 06 and 08)
GITHUB_TOKEN="..."
GITHUB_ENDPOINT="https://models.inference.ai.azure.com"
GITHUB_MODEL_ID="gpt-4o-mini"

# Bing grounding (Lesson 08 conditional workflow)
BING_CONNECTION_ID="..."

00-course-setup/README.md updated with corresponding lookup instructions for each new variable.

Original prompt

---

This section details on the original issue you should resolve

<issue_title>Standardize environment variable naming convention across all code samples</issue_title>
<issue_description>## Environment Variable Standardization

Currently, the repository uses inconsistent environment variable naming conventions across different code samples and chapters. This creates confusion for learners and makes the setup process more complex than necessary.

Problem

• Different samples use varying naming patterns (e.g., AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME vs MODEL_DEPLOYMENT_NAME)
• Inconsistent variable names between .env.example files and actual code usage
• Learners need to manage multiple different variable names for similar purposes
• Setup instructions become unclear due to naming variations

Proposed Solution

1. Standardize variable naming across all samples

   • Use consistent, descriptive names (e.g., AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY)
   • Follow a unified naming convention (Azure service prefix + descriptive suffix)

2. Update root .env.example to reflect standardized names

3. Implement backward compatibility with fallback logic

   deployment_name = os.getenv("MODEL_DEPLOYMENT_NAME") or os.getenv("AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME")

4. Update all English code samples to use the new standardized names

Benefits

✅ Improved developer experience with consistent variable naming
✅ Simplified setup process for beginners
✅ Reduced confusion and support requests
✅ Better maintainability across the repository
✅ Future-proof naming convention for new samples

Scope

• Root .env.example file
• All code samples in English chapters (01-11)
• Documentation updates where environment variables are referenced
• Maintain backward compatibility to avoid breaking existing setups

Priority

Medium - improves user experience but doesn't break existing functionality

Labels

enhancement documentation good first issue</issue_description>

Comments on the Issue (you are @copilot in this section)

• Fixes Standardize environment variable naming convention across all code samples #249

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Pull request overview

Standardizes environment variable naming in lesson docs/notebooks and fixes a recurring AzureAIProjectAgentProvider(...) syntax issue by removing stray ,) fragments and relying on env-based auto-configuration.

Changes:

• Fixed invalid AzureAIProjectAgentProvider instantiations across notebooks/READMEs by removing dangling keyword args caused by a stray ,).
• Updated READMEs to remove/replace legacy AZURE_OPENAI_* variables in favor of .env.example’s AZURE_AI_* convention.
• Expanded root .env.example and setup docs to include GitHub Models and Bing grounding variables used in later lessons.

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
12-context-engineering/code_samples/12-chat_summarization.ipynb	Fixes provider instantiation syntax and updates messaging to “Azure AI Foundry provider”.
10-ai-agents-production/code_samples/10-python-agent-framework.ipynb	Fixes provider instantiation syntax to a valid single call.
07-planning-design/README.md	Removes incorrect provider args and legacy env vars; uses provider env auto-detection.
06-building-trustworthy-agents/README.md	Removes unsupported/incorrect endpoint/model args from provider constructor.
04-tool-use/README.md	Replaces env var name in an AzureOpenAI client snippet and fixes provider instantiation syntax.
02-explore-agentic-frameworks/README.md	Fixes repeated provider instantiation syntax errors.
00-course-setup/README.md	Documents newly added .env variables for GitHub Models and Bing grounding.
.env.example	Adds GitHub Models + Bing grounding variables and clarifies Foundry project endpoint comment.

Comments suppressed due to low confidence (1)

.env.example:6

• The default AZURE_AI_MODEL_DEPLOYMENT_NAME example value (gpt-4o) appears inconsistent with several lesson snippets that reference gpt-4o-mini. To reduce learner confusion, consider aligning the example value with the model used most frequently in the lessons (or explicitly note that it must match the user’s deployment name and may be gpt-4o-mini depending on what they deployed).

# Model deployment name in your Foundry project (e.g., gpt-4o)
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o"

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[Copilot]

AzureOpenAI(azure_endpoint=...) expects an Azure OpenAI resource endpoint (e.g., https://<resource>.openai.azure.com), not an Azure AI Foundry project endpoint. Using AZURE_AI_PROJECT_ENDPOINT here will misconfigure the client. Consider reverting this line to os.getenv("AZURE_OPENAI_ENDPOINT") (and keep that variable in docs for AzureOpenAI snippets), or switch the example to use the Foundry/Agent provider API consistently instead of AzureOpenAI.

Suggested change

	azure_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT"),
	azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),