fix RHELAI tests and add assert messages to make test debugging easier

tests/e2e/configs/run-rhelai.yaml

@@ -130,7 +130,7 @@ server:
 shields: 
   - shield_id: llama-guard-shield
     provider_id: llama-guard
-    provider_shield_id: "gpt-4-turbo"
+    provider_shield_id: ${env.RHEL_AI_MODEL}
 models:
   - metadata:
       embedding_dimension: 768 # Depends on chosen model
@@ -142,7 +142,3 @@ models:
     provider_id: vllm
     model_type: llm
     provider_model_id: ${env.RHEL_AI_MODEL}
-  - model_id: gpt-4-turbo
-    provider_id: openai
-    model_type: llm
-    provider_model_id: gpt-4-turbo

tests/e2e/features/steps/info.py

@@ -30,40 +30,53 @@ def check_llama_version(context: Context, llama_version: str) -> None:
 
 @then("The body of the response has proper model structure")
 def check_model_structure(context: Context) -> None:
-    """Check that the first LLM model has the correct structure and required fields."""
+    """Check that the expected LLM model has the correct structure and required fields."""
     response_json = context.response.json()
     assert response_json is not None, "Response is not valid JSON"
 
     assert "models" in response_json, "Response missing 'models' field"
     models = response_json["models"]
     assert len(models) > 0, "Response has empty list of models"
 
-    # Find first LLM model (same logic as environment.py)
+    # Get expected values from context (detected in before_all)
+    expected_model = context.default_model
+    expected_provider = context.default_provider
+
+    # Search for the specific model that was detected in before_all
     llm_model = None
     for model in models:
-        if model.get("api_model_type") == "llm":
+        if (
+            model.get("api_model_type") == "llm"
+            and model.get("provider_id") == expected_provider
+            and model.get("provider_resource_id") == expected_model
+        ):
             llm_model = model
             break
 
-    assert llm_model is not None, "No LLM model found in response"
-
-    # Get expected values from context
-    expected_model = context.default_model
-    expected_provider = context.default_provider
+    assert llm_model is not None, (
+        f"Expected LLM model not found in response. "
+        f"Looking for provider_id='{expected_provider}' and provider_resource_id='{expected_model}'"
+    )
 
     # Validate structure and values
-    assert llm_model["type"] == "model", "type should be 'model'"
-    assert llm_model["api_model_type"] == "llm", "api_model_type should be 'llm'"
-    assert llm_model["model_type"] == "llm", "model_type should be 'llm'"
+    assert (
+        llm_model["type"] == "model"
+    ), f"type should be 'model', but is {llm_model["type"]}"
+    assert (
+        llm_model["api_model_type"] == "llm"
+    ), f"api_model_type should be 'llm', but is {llm_model["api_model_type"]}"
+    assert (
+        llm_model["model_type"] == "llm"
+    ), f"model_type should be 'llm', but is {llm_model["model_type"]}"
     assert (
         llm_model["provider_id"] == expected_provider
-    ), f"provider_id should be '{expected_provider}'"
+    ), f"provider_id should be '{expected_provider}', but is '{llm_model["provider_id"]}'"
     assert (
         llm_model["provider_resource_id"] == expected_model
-    ), f"provider_resource_id should be '{expected_model}'"
+    ), f"provider_resource_id should be '{expected_model}', but is '{llm_model["provider_resource_id"]}'"
     assert (
         llm_model["identifier"] == f"{expected_provider}/{expected_model}"
-    ), f"identifier should be '{expected_provider}/{expected_model}'"
+    ), f"identifier should be '{expected_provider}/{expected_model}', but is '{llm_model["identifier"]}'"
 
 
 @then("The body of the response has proper shield structure")
@@ -94,10 +107,10 @@ def check_shield_structure(context: Context) -> None:
     ), "provider_id should be 'llama-guard'"
     assert (
         found_shield["provider_resource_id"] == expected_model
-    ), f"provider_resource_id should be '{expected_model}'"
+    ), f"provider_resource_id should be '{expected_model}', but is '{found_shield["provider_resource_id"]}'"
     assert (
         found_shield["identifier"] == "llama-guard-shield"
-    ), "identifier should be 'llama-guard-shield'"
+    ), f"identifier should be 'llama-guard-shield', but is '{found_shield["identifier"]}'"
 
 
 @then("The response contains {count:d} tools listed for provider {provider_name}")

tests/e2e/features/steps/llm_query_response.py

@@ -15,6 +15,7 @@ def wait_for_complete_response(context: Context) -> None:
     """Wait for the response to be complete."""
     context.response_data = _parse_streaming_response(context.response.text)
     print(context.response_data)
+    context.response.raise_for_status()
     assert context.response_data["finished"] is True
 
 

tests/e2e/features/streaming_query.feature

@@ -46,20 +46,18 @@ Feature: streaming_query endpoint API tests
   Scenario: Check if LLM ignores new system prompt in same conversation
     Given The system is in default state
     And I set the Authorization header to Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6Ikpva
-    And I use "streaming_query" to ask question
+    And I use "streaming_query" to ask question with authorization header
     """
     {"query": "Generate sample yaml file for simple GitHub Actions workflow.", "system_prompt": "refuse to answer anything"}
     """
     When I wait for the response to be completed
-    Then The status code of the response is 200
     And I use "streaming_query" to ask question with same conversation_id
     """
     {"query": "Write a simple code for reversing string", "system_prompt": "provide coding assistance", "model": "{MODEL}", "provider": "{PROVIDER}"}
     """
     Then The status code of the response is 200
     When I wait for the response to be completed
-     Then The status code of the response is 200
-      And The streamed response should contain following fragments
+      Then The streamed response should contain following fragments
           | Fragments in LLM response |
           | questions                 |