Merge pull request #175 from microsoft/tech-connect-sql

fix: Remove custom conversation cache from stream_openai_text_workshop

Author: Prajwal-Microsoft

azure.yaml

@@ -16,10 +16,13 @@ hooks:
   postprovision:
     windows:
       run: |
-        Write-Host "Web app URL: "
-        Write-Host "$env:WEB_APP_URL" -ForegroundColor Cyan
-
         $isWorkshop = $env:IS_WORKSHOP
+        $deployApp = $env:AZURE_ENV_DEPLOY_APP
+
+        if (-not ($isWorkshop -eq "true" -and $deployApp -ne "true")) {
+            Write-Host "Web app URL: "
+            Write-Host "$env:WEB_APP_URL" -ForegroundColor Cyan
+        }
         if ($isWorkshop -eq "true") {
             Write-Host "`n=== Run each command one at a time, waiting for completion before proceeding ===" -ForegroundColor Yellow
             Write-Host "`n1. Setup Python environment:"
@@ -28,7 +31,12 @@ hooks:
             Write-Host "   pip install uv && uv pip install -r scripts/requirements.txt" -ForegroundColor Cyan
 
             Write-Host "`n2. Build solution (data processing and agent creation):"
-            Write-Host "   python scripts/00_build_solution.py --from 02" -ForegroundColor Cyan
+            $azureEnvOnly = $env:AZURE_ENV_ONLY
+            if ($azureEnvOnly -eq "true") {
+                Write-Host "   python scripts/00_build_solution.py --from 04" -ForegroundColor Cyan
+            } else {
+                Write-Host "   python scripts/00_build_solution.py --from 02" -ForegroundColor Cyan
+            }
 
             Write-Host "`nFor full instructions, see: https://microsoft.github.io/agentic-applications-for-unified-data-foundation-solution-accelerator/01-deploy/03-configure/" -ForegroundColor Gray
         } else {
@@ -45,9 +53,11 @@ hooks:
       interactive: true
     posix:
       run: |
-        echo "Web app URL: "
-        echo $WEB_APP_URL
-        echo ""
+        if [ "$IS_WORKSHOP" != "true" ] || [ "$AZURE_ENV_DEPLOY_APP" = "true" ]; then
+            echo "Web app URL: "
+            echo $WEB_APP_URL
+            echo ""
+        fi
 
         if [ "$IS_WORKSHOP" = "true" ]; then
             echo ""
@@ -60,7 +70,11 @@ hooks:
 
             echo ""
             echo "2. Build solution (data processing and agent creation):"
-            echo "   python scripts/00_build_solution.py --from 02"
+            if [ "$AZURE_ENV_ONLY" = "true" ]; then
+                echo "   python scripts/00_build_solution.py --from 04"
+            else
+                echo "   python scripts/00_build_solution.py --from 02"
+            fi
 
             echo ""
             echo "For full instructions, see: https://microsoft.github.io/agentic-applications-for-unified-data-foundation-solution-accelerator/01-deploy/03-configure/"

scripts/01_generate_data.py

@@ -587,7 +587,7 @@ def create_pdf(title, sections, filename):
      python scripts/04_generate_agent_prompt.py
      python scripts/06_upload_to_search.py
      python scripts/07_create_foundry_agent.py
-     python scripts/08_test_foundry_agent.py
+     python scripts/08_test_agent.py
 """)
 
 # ============================================================================

src/api/python/chat.py

@@ -235,6 +235,7 @@ async def stream_openai_text(conversation_id: str, query: str) -> StreamingRespo
             logger.info("No response received from OpenAI.")
             yield "I cannot answer this question with the current data. Please rephrase or add more details."
 
+
 async def stream_openai_text_workshop(conversation_id: str, query: str) -> StreamingResponse:
     """
     Get a streaming text response from OpenAI with workshop mode using responses.create().
@@ -256,55 +257,45 @@ async def stream_openai_text_workshop(conversation_id: str, query: str) -> Strea
             credential=credential
         ) as project_client:
 
-            cache = get_thread_cache()
-            conv_id = cache.get(conversation_id, None)
-            
             # Get database connection based on AZURE_ENV_ONLY flag
             from history_sql import SqlQueryTool, get_azure_sql_connection, get_fabric_db_connection
-            
+
             if AZURE_ENV_ONLY:
                 logger.info("Workshop mode: Using Azure SQL Database")
                 db_connection = await get_azure_sql_connection()
             else:
                 logger.info("Workshop mode: Using Fabric Lakehouse SQL")
                 db_connection = await get_fabric_db_connection()
-            
+
             if not db_connection:
                 logger.error("Failed to establish database connection")
                 raise Exception("Database connection failed")
-            
+
             custom_tool = SqlQueryTool(pyodbc_conn=db_connection)
 
             openai_client = project_client.get_openai_client()
-            
-            # Create or retrieve conversation
-            if not conv_id:
-                conv = await openai_client.conversations.create()
-                conv_id = conv.id
-                cache[conversation_id] = conv_id
 
             # Initial request to the agent
             response = await openai_client.responses.create(
-                conversation=conv_id,
                 input=query,
                 extra_body={"agent": {"name": os.getenv("AGENT_NAME_CHAT"), "type": "agent_reference"}}
             )
-            
+
             # Process response - handle function calls and search
             max_iterations = 10
             iteration = 0
-            
+
             while iteration < max_iterations:
                 iteration += 1
-                
+
                 # Check for function calls and tool uses in output
                 function_calls = []
                 text_output = ""
                 search_results = []
-                
+
                 for item in response.output:
                     item_type = getattr(item, 'type', None)
-                    
+
                     if item_type == 'function_call':
                         function_calls.append(item)
                     elif item_type == 'message':
@@ -332,22 +323,22 @@ async def stream_openai_text_workshop(conversation_id: str, query: str) -> Strea
                     # Handle search tool output (result)
                     elif item_type == 'azure_ai_search_call_output':
                         logger.info("AI Search completed")
-                
+
                 # If no function calls, we're done
                 if not function_calls:
                     if text_output:
                         complete_response += text_output
                         yield text_output
                     break
-                
+
                 # Handle function calls
                 tool_outputs = []
                 for fc in function_calls:
                     func_name = fc.name
                     func_args = json.loads(fc.arguments)
-                    
+
                     logger.info("Calling function: %s", func_name)
-                    
+
                     if func_name == "execute_sql":
                         sql_query = func_args.get("sql_query", "")
                         logger.info("Executing SQL query: %s", sql_query[:100])
@@ -363,13 +354,13 @@ async def stream_openai_text_workshop(conversation_id: str, query: str) -> Strea
                     else:
                         result_str = f"Unknown function: {func_name}"
                         logger.warning("Unknown function called: %s", func_name)
-                    
+
                     tool_outputs.append({
                         "type": "function_call_output",
                         "call_id": fc.call_id,
                         "output": result_str
                     })
-                
+
                 # Submit tool outputs and get next response
                 # Note: Don't include 'conversation' when using 'previous_response_id'
                 response = await openai_client.responses.create(
@@ -379,19 +370,14 @@ async def stream_openai_text_workshop(conversation_id: str, query: str) -> Strea
                         "previous_response_id": response.id
                     }
                 )
-            
+
             if iteration >= max_iterations:
                 logger.warning("Max iterations reached in workshop mode")
                 yield "\n\n(Response processing reached maximum iterations)"
 
     except Exception as e:
         complete_response = str(e)
         logger.error("Error in stream_openai_text_workshop: %s", e)
-        cache = get_thread_cache()
-        conv_id = cache.pop(conversation_id, None)
-        if conv_id is not None:
-            corrupt_key = f"{conversation_id}_corrupt_{random.randint(1000, 9999)}"
-            cache[corrupt_key] = conv_id
         raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Error streaming OpenAI text") from e
 
     finally:

src/api/python/history.py

@@ -650,7 +650,7 @@ async def get_conversation_messages(user_id: str, conversation_id: str):
             else:
                 message_content = content
                 citations = ""
-            
+
             messages.append({
                 "id": msg["id"],
                 "role": msg["role"],
@@ -862,7 +862,7 @@ async def delete_conversation_route(request: Request, id: str = Query(...)):
         authenticated_user = get_authenticated_user_details(
             request_headers=request.headers)
         user_id = authenticated_user["user_principal_id"]
-        
+
         conversation_id = id
         if not conversation_id:
             track_event_if_configured("DeleteConversationValidationError", {

src/api/python/history_sql.py

@@ -79,7 +79,7 @@ def track_event_if_configured(event_name: str, event_data: dict):
 async def get_azure_sql_connection():
     """
     Get a connection to Azure SQL Server using DefaultAzureCredential.
-    
+
     Returns:
         Connection: Database connection object for Azure SQL.
     """
@@ -88,19 +88,19 @@ async def get_azure_sql_connection():
     driver18 = "ODBC Driver 18 for SQL Server"
     driver17 = "ODBC Driver 17 for SQL Server"
     api_uid = os.getenv("API_UID", "")
-    
+
     credential = await get_azure_credential_async(client_id=api_uid)
     token = await credential.get_token("https://database.windows.net/.default")
     await credential.close()
-    
+
     token_bytes = token.token.encode("utf-16-LE")
     token_struct = struct.pack(
         f"<I{len(token_bytes)}s",
         len(token_bytes),
         token_bytes
     )
     SQL_COPT_SS_ACCESS_TOKEN = 1256
-    
+
     try:
         connection_string = f"DRIVER={{{driver18}}};SERVER={sql_server};DATABASE={sql_database};"
         conn = pyodbc.connect(connection_string, attrs_before={SQL_COPT_SS_ACCESS_TOKEN: token_struct})
@@ -176,15 +176,15 @@ async def get_fabric_db_connection():
 async def get_db_connection():
     """
     Get a database connection based on deployment mode.
-    
+
     When IS_WORKSHOP is true, uses Azure SQL Server.
     When IS_WORKSHOP is false or not set, uses Fabric SQL.
 
     Returns:
         Connection: Database connection object, or None if connection fails.
     """
     is_workshop = os.getenv("IS_WORKSHOP", "false").lower() == "true"
-    
+
     if is_workshop:
         logging.info("Workshop deployment mode: Using Azure SQL Server")
         return await get_azure_sql_connection()

workshop/docs/01-deploy/04-run-scenario.md

@@ -39,7 +39,7 @@ This uses the `data/default` folder and runs all setup steps:
 ------------------------------------------------------------
 [OK] Pipeline completed successfully!
 
-Next: python scripts/08_test_foundry_agent.py
+Next: python scripts/08_test_agent.py
 
 ```
 
@@ -57,7 +57,7 @@ Support group by in GQL.
 ## Test the Agent
 
 ```bash
-python scripts/08_test_foundry_agent.py
+python scripts/08_test_agent.py
 ```
 
 

workshop/docs/02-customize/03-demo.md

@@ -5,12 +5,7 @@
 After generation completes, test the agent:
 
 ```bash
-python scripts/08_test_foundry_agent.py
-```
-
-No Fabric license? Run the following command: 
-```bash
-python scripts/08_test_foundry_agent.py --foundry-only
+python scripts/08_test_agent.py
 ```
 
 ## Use the Generated Sample Questions