use getenv with default value

Author: john0isaac

src/quartapp/app.py

@@ -86,6 +86,6 @@ async def chat_handler() -> Any:
 # Or specify port manually:
 """
 if __name__ == '__main__':
-    port = int(os.environ.get('PORT', 5000))
+    port = int(os.getenv('PORT', 5000))
     app.run(host='0.0.0.0', port=port)
 """

src/quartapp/rag.py

@@ -36,8 +36,8 @@
 
 
 # collection name will be used multiple times in the code so we store it in a variable
-database_name = os.getenv("AZURE_COSMOS_DATABASE_NAME")
-collection_name = os.getenv("AZURE_COSMOS_COLLECTION_NAME")
+database_name = os.getenv("AZURE_COSMOS_DATABASE_NAME", "semanticKernel")
+collection_name = os.getenv("AZURE_COSMOS_COLLECTION_NAME", "textMemory")
 
 # Vector search index parameters
 index_name = os.getenv("AZURE_COSMOS_INDEX_NAME", "VectorSearchIndex")
@@ -52,8 +52,8 @@
 
 def get_mongo_connection_string() -> str:
     mongo_connection_string = os.getenv("AZURE_COSMOS_CONNECTION_STRING", "<YOUR-COSMOS-DB-CONNECTION-STRING>")
-    mongo_username = quote_plus(os.getenv("AZURE_COSMOS_USERNAME"))
-    mongo_password = quote_plus(os.getenv("AZURE_COSMOS_PASSWORD"))
+    mongo_username = quote_plus(os.getenv("AZURE_COSMOS_USERNAME", "admin"))
+    mongo_password = quote_plus(os.getenv("AZURE_COSMOS_PASSWORD", "password"))
     return mongo_connection_string.replace("<user>", mongo_username).replace("<password>", mongo_password)
 
 
@@ -76,9 +76,9 @@ async def prompt_with_rag_or_vector(query_term: str, option: str) -> str:
 def initialize_sk_chat_embedding() -> Kernel:
     kernel = Kernel()
     # adding azure openai chat service
-    chat_model_deployment_name = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME") or "chat-deployment"
-    endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") or "https://test-endpoint.openai.com/"
-    api_key = os.environ.get("AZURE_OPENAI_API_KEY") or "VerySecretApiKey"
+    chat_model_deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "chat-deployment")
+    endpoint = os.getenv("AZURE_OPENAI_ENDPOINT", "https://test-endpoint.openai.com/")
+    api_key = os.getenv("AZURE_OPENAI_API_KEY", "VerySecretApiKey")
 
     kernel.add_service(
         AzureChatCompletion(
@@ -91,9 +91,7 @@ def initialize_sk_chat_embedding() -> Kernel:
     logging.info("Added Azure OpenAI Chat Service...")
 
     # adding azure openai text embedding service
-    embedding_model_deployment_name = (
-        os.environ.get("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME") or "embedding-deployment"
-    )
+    embedding_model_deployment_name = os.getenv("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME", "embedding-deployment")
 
     kernel.add_service(
         AzureTextEmbedding(
@@ -117,11 +115,11 @@ async def initialize_sk_memory_store(
     try:
         logging.info("Creating or updating Azure Cosmos DB Memory Store...")
         store = await AzureCosmosDBMemoryStore.create(
-            cosmos_connstr=get_mongo_connection_string() or "mongodb://localhost:27017",
+            cosmos_connstr=get_mongo_connection_string(),
             cosmos_api="mongo-vcore",
-            database_name=database_name or "semanticKernel",
-            collection_name=collection_name or "textMemory",
-            index_name=index_name or "VectorSearchIndex",
+            database_name=database_name,
+            collection_name=collection_name,
+            index_name=index_name,
             vector_dimensions=vector_dimensions,
             num_lists=num_lists,
             similarity=similarity,
@@ -160,7 +158,7 @@ async def grounded_response(kernel: Kernel) -> KernelFunction:
     User: {{$query_term}}
     Chatbot:"""
 
-    chat_model_deployment_name = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME")
+    chat_model_deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "chat-deployment")
 
     execution_settings = OpenAITextPromptExecutionSettings(
         service_id="chat_completion",

src/scripts/add_data.py

@@ -49,7 +49,7 @@ async def upsert_data_to_memory_store(
     description_field_name: str,
 ) -> None:
     # collection name will be used multiple times in the code so we store it in a variable
-    collection_name: str = os.environ.get("AZURE_COSMOS_COLLECTION_NAME") or "sk_collection"
+    collection_name: str = os.getenv("AZURE_COSMOS_COLLECTION_NAME", "sk_collection")
 
     with open(file=data_file_path, encoding="utf-8") as f:
         data = json.load(f)