fixed formatting issues

Author: nuwangeek

src/llm_orchestration_service.py

@@ -2192,7 +2192,7 @@ def _generate_rag_response(
                     },
                     output=answer,
                 )
-            
+
             retrieval_context: List[Dict[str, Any]] | None = None
             if eval_mode and relevant_chunks:
                 max_blocks_used = ResponseGenerationConstants.DEFAULT_MAX_BLOCKS
@@ -2230,16 +2230,16 @@ def _generate_rag_response(
                         chunks=None,  # No chunks when question is out of scope
                     )
                 else:
-                    response =  OrchestrationResponse(
+                    response = OrchestrationResponse(
                         chatId=request.chatId,
                         llmServiceActive=True,  # service OK; insufficient context
                         questionOutOfLLMScope=True,
                         inputGuardFailed=False,
                         content=localized_msg,
                     )
-                    if eval_mode: 
+                    if eval_mode:
                         response.retrieval_context = retrieval_context
-                    return response                    
+                    return response
 
             # In-scope: return the answer as-is (NO citations)
             logger.info("Returning in-scope answer without citations.")
@@ -2264,14 +2264,14 @@ def _generate_rag_response(
                     chunks=self._format_chunks_for_test_response(relevant_chunks),
                 )
             else:
-                response =  OrchestrationResponse(
+                response = OrchestrationResponse(
                     chatId=request.chatId,
                     llmServiceActive=True,
                     questionOutOfLLMScope=False,
                     inputGuardFailed=False,
                     content=content_with_refs,
                 )
-                if eval_mode: 
+                if eval_mode:
                     response.retrieval_context = retrieval_context
                 return response
 

src/llm_orchestration_service_api.py

@@ -1,4 +1,5 @@
 """LLM Orchestration Service API - FastAPI application."""
+
 import os
 from contextlib import asynccontextmanager
 from typing import Any, AsyncGenerator, Dict
@@ -40,7 +41,7 @@
     ContextGenerationRequest,
     ContextGenerationResponse,
     EmbeddingErrorResponse,
-    DeepEvalTestOrchestrationResponse
+    DeepEvalTestOrchestrationResponse,
 )
 
 

src/models/request_models.py

@@ -278,4 +278,4 @@ class DeepEvalTestOrchestrationResponse(BaseModel):
     content: str
     retrieval_context: Optional[List[Dict[str, Any]]] = None
     refined_questions: Optional[List[str]] = None
-    expected_output: Optional[str] = None  # For DeepEval
+    expected_output: Optional[str] = None  # For DeepEval

tests/conftest.py

@@ -16,14 +16,11 @@
 
 
 def download_embeddings_from_azure(
-    connection_string: str,
-    container_name: str,
-    blob_name: str,
-    local_path: Path
+    connection_string: str, container_name: str, blob_name: str, local_path: Path
 ) -> None:
     """
     Download pre-computed embeddings from Azure Blob Storage.
-    
+
     Args:
         connection_string: Azure Storage connection string
         container_name: Name of the blob container
@@ -34,28 +31,29 @@ def download_embeddings_from_azure(
     logger.info(f"  Container: {container_name}")
     logger.info(f"  Blob: {blob_name}")
     logger.info(f"  Local path: {local_path}")
-    
+
     try:
         # Create BlobServiceClient
-        blob_service_client = BlobServiceClient.from_connection_string(connection_string)
-        
+        blob_service_client = BlobServiceClient.from_connection_string(
+            connection_string
+        )
+
         # Get blob client
         blob_client = blob_service_client.get_blob_client(
-            container=container_name,
-            blob=blob_name
+            container=container_name, blob=blob_name
         )
-        
+
         # Ensure parent directory exists
         local_path.parent.mkdir(parents=True, exist_ok=True)
-        
+
         # Download the blob
         with open(local_path, "wb") as download_file:
             download_stream = blob_client.download_blob()
             download_file.write(download_stream.readall())
-        
+
         file_size_kb = local_path.stat().st_size / 1024
         logger.info(f"✓ Downloaded embeddings successfully ({file_size_kb:.2f} KB)")
-        
+
     except Exception as e:
         logger.error(f"Failed to download embeddings from Azure: {e}")
         raise
@@ -144,10 +142,10 @@ def start(self) -> None:
         """Start all test containers and bootstrap Vault"""
         logger.info("Starting RAG Stack testcontainers...")
         os.environ["EVAL_MODE"] = "true"
-        
+
         # Download embeddings from Azure before starting containers
         self._download_embeddings_from_azure()
-        
+
         # Prepare Vault Agent directories
         agent_in = self.project_root / "test-vault" / "agents" / "llm"
         agent_out = self.project_root / "test-vault" / "agent-out"
@@ -212,33 +210,33 @@ def _download_embeddings_from_azure(self) -> None:
         connection_string = os.getenv("AZURE_STORAGE_CONNECTION_STRING")
         container_name = os.getenv("AZURE_STORAGE_CONTAINER_NAME", "test-embeddings")
         blob_name = os.getenv("AZURE_STORAGE_BLOB_NAME", "test_embeddings.json")
-        
+
         # Local path where embeddings should be saved
         embeddings_file = self.project_root / "tests" / "data" / "test_embeddings.json"
-        
+
         # Skip if embeddings already exist locally (for local development)
-        #if embeddings_file.exists():
+        # if embeddings_file.exists():
         #    logger.info("Embeddings file already exists locally, skipping Azure download")
         #    return
-        
+
         # Require Azure configuration for CI/CD
         if not connection_string:
             raise ValueError(
                 "AZURE_STORAGE_CONNECTION_STRING is required to download embeddings. "
                 "Either set this environment variable or ensure test_embeddings.json "
                 f"exists at {embeddings_file}"
             )
-        
+
         logger.info("=" * 80)
         logger.info("DOWNLOADING EMBEDDINGS FROM AZURE BLOB STORAGE")
         logger.info("=" * 80)
-        
+
         try:
             download_embeddings_from_azure(
                 connection_string=connection_string,
                 container_name=container_name,
                 blob_name=blob_name,
-                local_path=embeddings_file
+                local_path=embeddings_file,
             )
             logger.info("Embeddings download complete")
         except Exception as e:
@@ -797,8 +795,9 @@ def orchestration_client(rag_stack: RAGStackTestContainers):
     Function-scoped fixture that provides the orchestration service URL.
     Tests can use either requests (sync) or httpx (async).
     """
+
     class OrchestrationClient:
         def __init__(self, base_url: str):
             self.base_url = base_url
-    
-    return OrchestrationClient(rag_stack.get_orchestration_service_url())
+
+    return OrchestrationClient(rag_stack.get_orchestration_service_url())

tests/deepeval_tests/report_generator.py

@@ -161,13 +161,10 @@ def generate_failure_analysis(results: Dict[str, Any]) -> str:
             if len(failure["input"]) > 50
             else failure["input"]
         )
-        reason_preview = (
-            failure["reason"] )
+        reason_preview = failure["reason"]
 
         analysis += f"| {failure['test_case']} | {query_preview} | {failure['metric']} | {failure['score']:.2f} | {reason_preview} |\n"
 
-
-
     analysis += "\n"
     return analysis
 

tests/deepeval_tests/standard_tests.py

@@ -113,6 +113,7 @@ def save_results_fixture():
 import httpx  # Replace requests with httpx
 import asyncio
 
+
 class TestRAGSystem:
     """Test suite for RAG system evaluation using DeepEval metrics via API."""
 
@@ -177,7 +178,10 @@ async def test_all_metrics(self, test_item: Dict[str, Any], orchestration_client
             except httpx.RequestError as e:
                 result = {"content": f"API Error: {str(e)}", "retrieval_context": []}
             except Exception as e:
-                result = {"content": f"Unexpected error: {str(e)}", "retrieval_context": []}
+                result = {
+                    "content": f"Unexpected error: {str(e)}",
+                    "retrieval_context": [],
+                }
         if result is None:
             result = {"content": "No response received", "retrieval_context": []}
         # --- DEBUG LOGGING ---
@@ -186,20 +190,23 @@ async def test_all_metrics(self, test_item: Dict[str, Any], orchestration_client
         print("=" * 80)
         print(f"Response keys: {list(result.keys())}")
         for key, value in result.items():
-            
             print(key, value)
         print(f"Content length: {len(result.get('content', ''))}")
         print(f"Retrieval context: {len(result.get('retrieval_context', []))} chunks")
-        
-        if result.get('retrieval_context'):
-            for chunk in result['retrieval_context']:
+
+        if result.get("retrieval_context"):
+            for chunk in result["retrieval_context"]:
                 print(chunk.keys())
-                context = chunk.get('content', '') if isinstance(chunk, dict) else str(chunk)
-                meta = chunk.get('metadata', {}) if isinstance(chunk, dict) else {}
-                fused_score = meta.get('fused_score', 'N/A')
-                bm25_score = meta.get('bm25_score', 'N/A')
-                semantic_score = meta.get('semantic_score', 'N/A')
-                print(f"Chunk (fused: {fused_score}, bm25: {bm25_score}, semantic: {semantic_score}):\n {context}\n\n")
+                context = (
+                    chunk.get("content", "") if isinstance(chunk, dict) else str(chunk)
+                )
+                meta = chunk.get("metadata", {}) if isinstance(chunk, dict) else {}
+                fused_score = meta.get("fused_score", "N/A")
+                bm25_score = meta.get("bm25_score", "N/A")
+                semantic_score = meta.get("semantic_score", "N/A")
+                print(
+                    f"Chunk (fused: {fused_score}, bm25: {bm25_score}, semantic: {semantic_score}):\n {context}\n\n"
+                )
         else:
             print("WARNING: No retrieval context returned!")
         print("=" * 80)
@@ -262,4 +269,6 @@ async def run_metric(metric_name, metric):
         # --- Assert ---
         failed = [name for name, res in metrics_results.items() if not res["passed"]]
         if failed:
-            pytest.fail(f"Metrics failed: {', '.join(failed)} for input: {test_item['input'][:50]}")
+            pytest.fail(
+                f"Metrics failed: {', '.join(failed)} for input: {test_item['input'][:50]}"
+            )