Merge pull request #169 from microsoft/tech-connect-sql

chore: API, APP & doc changes

Author: Prajwal-Microsoft

azure.yaml

@@ -23,10 +23,9 @@ hooks:
         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:"
-            Write-Host "   cd scripts" -ForegroundColor Cyan
             Write-Host "   python -m venv .venv" -ForegroundColor Cyan
             Write-Host "   .venv\Scripts\activate" -ForegroundColor Cyan
-            Write-Host "   pip install uv && uv pip install -r requirements.txt" -ForegroundColor Cyan
+            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
@@ -55,10 +54,9 @@ hooks:
             echo "=== Run each command one at a time, waiting for completion before proceeding ==="
             echo ""
             echo "1. Setup Python environment:"
-            echo "   cd scripts"
             echo "   python -m venv .venv"
             echo "   source .venv/bin/activate"
-            echo "   pip install uv && uv pip install -r requirements.txt"
+            echo "   pip install uv && uv pip install -r scripts/requirements.txt"
 
             echo ""
             echo "2. Build solution (data processing and agent creation):"

data/default/config/agent_ids.json

@@ -2,7 +2,5 @@
   "_comment": "This file is populated when you run 07_create_foundry_agent.py",
   "chat_agent_id": null,
   "chat_agent_name": null,
-  "title_agent_id": null,
-  "title_agent_name": null,
   "search_index": null
 }

scripts/00_build_solution.py

@@ -3,16 +3,13 @@
 Master script that runs all steps to build the complete solution.
 
 Usage:
-    # Full Fabric mode (uses Fabric Lakehouse + AI Search)
+    # Run all steps from the beginning (uses either Fabric Lakehouse or Azure SQL + AI Search)
     python scripts/00_build_solution.py
-
-    # Azure-only mode (uses Azure SQL + AI Search, no Fabric required)
-    python scripts/00_build_solution.py --azure-only
     
     # Start from a specific step
     python scripts/00_build_solution.py --from 06
 
-Steps (Full mode):
+Steps (Fabric SQL mode):
     01  - Generate sample data
     02  - Create Fabric Lakehouse
     03  - Load data into Fabric
@@ -66,15 +63,11 @@
     formatter_class=argparse.RawDescriptionHelpFormatter,
     epilog="""
 Examples:
-  python scripts/00_build_solution.py                # Full Fabric mode
-  python scripts/00_build_solution.py --azure-only   # Azure SQL mode (no Fabric)
+  python scripts/00_build_solution.py                # Full Fabric mode or SQL mode
   python scripts/00_build_solution.py --from 06      # Start from step 06
   python scripts/00_build_solution.py --only 07      # Run only specific steps
 """
 )
-
-parser.add_argument("--azure-only", action="store_true",
-                    help="Use Azure SQL instead of Fabric Lakehouse")
 parser.add_argument("--industry", type=str, 
                     help="Industry for data generation (overrides .env)")
 parser.add_argument("--usecase", type=str, 
@@ -101,13 +94,20 @@
 # Quiet mode is default (verbose must be explicitly requested)
 args.quiet = not args.verbose
 
+# Load environment from azd + project .env
+from load_env import load_all_env
+load_all_env()
+
+# Get azure_only from environment variable (set AZURE_ENV_ONLY=true to use Azure SQL mode)
+azure_only = os.getenv("AZURE_ENV_ONLY", "false").lower() in ("true", "1", "yes")
+
 # ============================================================================
 # Determine Pipeline
 # ============================================================================
 
 if args.only:
     pipeline = args.only
-elif args.azure_only:
+elif azure_only:
     pipeline = AZURE_ONLY_PIPELINE.copy()
 else:
     pipeline = FABRIC_PIPELINE.copy()
@@ -135,10 +135,6 @@
         print(f"ERROR: Script not found: {STEPS[step]['script']}")
         sys.exit(1)
 
-# Load environment from azd + project .env
-from load_env import load_all_env
-load_all_env()
-
 # ============================================================================
 # Interactive Prompts for Data Generation
 # ============================================================================
@@ -180,7 +176,7 @@
 # Print Plan
 # ============================================================================
 
-mode = "Azure SQL" if args.azure_only else "Fabric"
+mode = "Azure SQL" if azure_only else "Fabric"
 print("\n" + "="*60)
 print(f"Build Solution Pipeline ({mode} Mode)")
 print("="*60)
@@ -231,7 +227,7 @@ def run_step(step_id):
     if step_id == "02" and args.clean:
         cmd.append("--clean")
 
-    if step_id == "07" and args.azure_only:
+    if step_id == "07" and azure_only:
         cmd.append("--azure-only")
 
     # Run the script

scripts/03_load_fabric_data.py

@@ -239,6 +239,77 @@ def wait_for_lro(operation_url, operation_name="Operation", timeout=300):
     print("  Waiting for tables to be indexed...")
     time.sleep(30)
 
+# ============================================================================
+# Step 4: Get Fabric SQL Endpoint and Update App Service
+# ============================================================================
+
+print(f"\n[4/4] Getting Fabric SQL endpoint...")
+
+def get_fabric_sql_endpoint():
+    """Get the SQL analytics endpoint for the Fabric Lakehouse"""
+    try:
+        url = f"{FABRIC_API}/workspaces/{WORKSPACE_ID}/lakehouses/{LAKEHOUSE_ID}"
+        resp = make_request("GET", url)
+        if resp.status_code == 200:
+            data = resp.json()
+            props = data.get("properties", {})
+            sql_props = props.get("sqlEndpointProperties", {})
+            return sql_props.get("connectionString")
+    except Exception as e:
+        print(f"  [WARN] Could not get Fabric SQL endpoint: {e}")
+    return None
+
+FABRIC_SQL_ENDPOINT = get_fabric_sql_endpoint()
+
+if FABRIC_SQL_ENDPOINT:
+    print(f"  [OK] SQL Endpoint: {FABRIC_SQL_ENDPOINT}")
+    
+    # Save to fabric_ids.json
+    fabric_ids["sql_endpoint"] = FABRIC_SQL_ENDPOINT
+    with open(fabric_ids_path, "w") as f:
+        json.dump(fabric_ids, f, indent=2)
+
+# Update App Service env vars
+subscription_id = os.getenv("AZURE_SUBSCRIPTION_ID")
+resource_group = os.getenv("RESOURCE_GROUP_NAME")
+app_name = os.getenv("API_APP_NAME")
+
+if subscription_id and resource_group and app_name:
+    try:
+        from azure.mgmt.web import WebSiteManagementClient
+        from azure.identity import DefaultAzureCredential
+        
+        web_credential = DefaultAzureCredential()
+        web_client = WebSiteManagementClient(web_credential, subscription_id)
+        
+        current = web_client.web_apps.list_application_settings(resource_group, app_name)
+        props = dict(current.properties or {})
+        
+        # Build full ODBC connection string
+        if FABRIC_SQL_ENDPOINT:
+            fabric_conn_string = f"DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={FABRIC_SQL_ENDPOINT};DATABASE={LAKEHOUSE_NAME};Encrypt=yes;TrustServerCertificate=no"
+        else:
+            fabric_conn_string = ""
+        
+        new_settings = {
+            "FABRIC_SQL_CONNECTION_STRING": fabric_conn_string
+        }
+        props.update(new_settings)
+        
+        web_client.web_apps.update_application_settings(
+            resource_group,
+            app_name,
+            {"properties": props}
+        )
+        
+        print(f"  [OK] App Service settings updated")
+    except Exception as e:
+        print(f"  [WARN] Failed to update App Service: {e}")
+else:
+    if FABRIC_SQL_ENDPOINT:
+        fabric_conn_string = f"DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={FABRIC_SQL_ENDPOINT};DATABASE={LAKEHOUSE_NAME};Encrypt=yes;TrustServerCertificate=no"
+        print(f"  NOTE: Set FABRIC_SQL_CONNECTION_STRING={fabric_conn_string} in App Service")
+
 # ============================================================================
 # Summary
 # ============================================================================
@@ -249,13 +320,10 @@ def wait_for_lro(operation_url, operation_name="Operation", timeout=300):
 print(f"""
 Uploaded {len(uploaded_files)} files: {', '.join(uploaded_files)}
 Tables loaded: {', '.join(ontology_config['tables'].keys())}
+SQL Endpoint: {FABRIC_SQL_ENDPOINT or 'Not available yet'}
 
 Next step - Generate schema prompt:
   python scripts/04_generate_agent_prompt.py
-
-To reload data later (e.g., with new/larger dataset):
-  python scripts/01_generate_sample_data.py --scenario <SCENARIO> --size medium
-  python scripts/03_load_fabric_data.py
 """)
 
 

scripts/07_create_agent.py

@@ -367,18 +367,14 @@ def build_agent_instructions(config, schema_text, use_fabric, config_dir):
         print(f"  Agent Name: {chat_agent.name}")
 
         # Delete existing title agent if it exists
-        print(f"\nChecking if title agent '{TITLE_AGENT_NAME}' already exists...")
         try:
             existing_title_agent = project_client.agents.get(TITLE_AGENT_NAME)
             if existing_title_agent:
-                print(f"  Found existing title agent, deleting...")
                 project_client.agents.delete(TITLE_AGENT_NAME)
-                print(f"[OK] Deleted existing title agent")
         except Exception:
-            print(f"  No existing title agent found")
+            pass
 
         # Create title agent
-        print(f"\nCreating title agent...")
         title_agent_definition = PromptAgentDefinition(
             model=MODEL,
             instructions=title_agent_instructions,
@@ -391,8 +387,6 @@ def build_agent_instructions(config, schema_text, use_fabric, config_dir):
         )
 
         print(f"\n[OK] Title agent created successfully!")
-        print(f"  Title Agent ID: {title_agent.id}")
-        print(f"  Title Agent Name: {title_agent.name}")
 
 except Exception as e:
     print(f"\n[FAIL] Failed to create agent: {e}")
@@ -413,8 +407,6 @@ def build_agent_instructions(config, schema_text, use_fabric, config_dir):
 # Save agent-specific info
 agent_ids["chat_agent_id"] = chat_agent.id
 agent_ids["chat_agent_name"] = chat_agent.name
-agent_ids["title_agent_id"] = title_agent.id
-agent_ids["title_agent_name"] = title_agent.name
 agent_ids["search_index"] = INDEX_NAME
 agent_ids["sql_mode"] = "fabric" if USE_FABRIC else "azure_sql"
 if not USE_FABRIC:

scripts/deployment_guide.pdf

@@ -123,11 +123,15 @@ def create_guide():
 
     pdf.step(5, 'Build the solution (~5 min)',
         'python scripts/00_build_solution.py --from 02',
-        'No Fabric? Use: python scripts/00_build_solution.py --from 04 --azure-only')
+        'No Fabric? Use: python scripts/00_build_solution.py --from 04')
 
     pdf.step(6, 'Test the agent',
         'python scripts/08_test_agent.py')
 
+    pdf.step(7, 'Deploy and launch the application',
+        'azd env set AZURE_ENV_DEPLOY_APP true\nazd up',
+        'After deployment completes, open the app URL shown in the output')
+    
     # Sample Questions
     pdf.section_header('Try These Questions')
     pdf.info_text('Structured: "How many outages occurred last month?" | "What is the average resolution time?"')

scripts/generate_deployment_guide.py

@@ -27,7 +27,7 @@ pypdf==5.6.0
 azure-search-documents==11.6.0
 
 # Azure SQL Management
-azure-mgmt.-web==7.3.1
+azure-mgmt-web==7.3.1
 
 # OpenAI SDK (for embeddings)
 openai==2.8.1