[WIP] Rename LLM_MODEL to AZURE_DEPLOYMENT_NAME for clarity

README.md

@@ -167,9 +167,9 @@ Each target specifies:
 - name: azure_base
   provider: azure
   settings:
-    endpoint: "AZURE_OPEN_AI_ENDPOINT"
-    api_key: "AZURE_OPEN_AI_API_KEY"
-    model: "LLM_MODEL"
+    endpoint: "AZURE_OPENAI_ENDPOINT"
+    api_key: "AZURE_OPENAI_API_KEY"
+    model: "AZURE_DEPLOYMENT_NAME"
 ```
 
 **Anthropic targets:**
@@ -178,7 +178,7 @@ Each target specifies:
   provider: anthropic
   settings:
     api_key: "ANTHROPIC_API_KEY"
-    model: "LLM_MODEL"
+    model: "ANTHROPIC_MODEL"
 ```
 
 **VS Code targets:**

docs/examples/simple/.bbeval/targets.yaml

@@ -15,15 +15,15 @@
   # The base LLM provider needs no extra settings, as the model is
   # defined in the .env file.
   settings:
-    endpoint: AZURE_OPEN_AI_ENDPOINT
-    api_key: AZURE_OPEN_AI_API_KEY
-    model: LLM_MODEL
+    endpoint: AZURE_OPENAI_ENDPOINT
+    api_key: AZURE_OPENAI_API_KEY
+    model: AZURE_DEPLOYMENT_NAME
 
 - name: default
   provider: azure
   # The base LLM provider needs no extra settings, as the model is
   # defined in the .env file.
   settings:
-    endpoint: AZURE_OPEN_AI_ENDPOINT
-    api_key: AZURE_OPEN_AI_API_KEY
-    model: LLM_MODEL
+    endpoint: AZURE_OPENAI_ENDPOINT
+    api_key: AZURE_OPENAI_API_KEY
+    model: AZURE_DEPLOYMENT_NAME

docs/examples/simple/.env.template

@@ -6,9 +6,9 @@ PROVIDER=azure  # or anthropic
 
 # Azure OpenAI Configuration
 # These are the default environment variable names used in the provided targets.yaml
-AZURE_OPEN_AI_ENDPOINT=https://your-endpoint.openai.azure.com/
-AZURE_OPEN_AI_API_KEY=your-api-key-here
-LLM_MODEL=gpt-5-chat  # or your deployment name
+AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
+AZURE_OPENAI_API_KEY=your-api-key-here
+AZURE_DEPLOYMENT_NAME=gpt-5-chat  # or your deployment name
 
 # Anthropic Configuration (if using Anthropic provider)
 ANTHROPIC_API_KEY=your-anthropic-api-key-here

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "bbeval"
-version = "0.1.9"
+version = "0.1.10"
 description = "A lightweight, extensible evaluator for testing model-generated responses"
 readme = "README.md"
 requires-python = ">=3.12"

src/bbeval/cli.py

@@ -97,13 +97,13 @@ def create_judge_model(target: Dict, targets: List[Dict], model: str, verbose: b
             print(f"  No judge_target specified, falling back to Azure")
         judge_provider = "azure"
         judge_settings = {
-            'endpoint': 'AZURE_OPEN_AI_ENDPOINT',
-            'api_key': 'AZURE_OPEN_AI_API_KEY'
+            'endpoint': 'AZURE_OPENAI_ENDPOINT',
+            'api_key': 'AZURE_OPENAI_API_KEY'
         }
         judge_model = model
 
         # Check if Azure credentials are available for fallback
-        if not os.getenv('AZURE_OPEN_AI_ENDPOINT') or not os.getenv('AZURE_OPEN_AI_API_KEY'):
+        if not os.getenv('AZURE_OPENAI_ENDPOINT') or not os.getenv('AZURE_OPENAI_API_KEY'):
             if verbose:
                 print(f"  Azure credentials not found, using mock judge")
             judge_provider = "mock"
@@ -494,7 +494,13 @@ def run_evaluation(test_file: str,
     provider = target['provider']
     settings = target.get('settings')
     # For DSPy configuration, we still need a model parameter (but not for results)
-    model = os.getenv('LLM_MODEL', 'gpt-4')
+    # Get model from target settings, resolving env var if needed
+    model = settings.get('model', 'AZURE_DEPLOYMENT_NAME') if settings else 'AZURE_DEPLOYMENT_NAME'
+    if isinstance(model, str) and model in os.environ:
+        model = os.getenv(model, 'gpt-4')
+    elif isinstance(model, str):
+        # Fallback if model string is not an env var name
+        model = 'gpt-4'
 
     # Configure model
     if dry_run:

src/diagnostics/azure_deployment_diag.py

@@ -12,9 +12,9 @@
 load_dotenv()
 
 # Get Azure OpenAI configuration
-endpoint = os.getenv("AZURE_OPEN_AI_ENDPOINT")
-api_key = os.getenv("AZURE_OPEN_AI_API_KEY")
-model = os.getenv("LLM_MODEL", "gpt-5-chat")
+endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+api_key = os.getenv("AZURE_OPENAI_API_KEY")
+model = os.getenv("AZURE_DEPLOYMENT_NAME", "gpt-5-chat")
 
 print(f"Endpoint: {endpoint}")
 print(f"API Key: {'***' + api_key[-4:] if api_key else 'None'}")