pre-warm ollama local model on initialization

scripts/run_worker.py

@@ -14,11 +14,11 @@ from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
 async def main():
     # Load environment variables
     load_dotenv(override=True)
-    
+
     # Print LLM configuration info
     llm_provider = os.environ.get("LLM_PROVIDER", "openai").lower()
     print(f"Worker will use LLM provider: {llm_provider}")
-    
+
     # Create the client
     client = await get_temporal_client()
 
@@ -26,6 +26,29 @@ async def main():
     activities = ToolActivities()
     print(f"ToolActivities initialized with LLM provider: {llm_provider}")
 
+    # If using Ollama, pre-load the model to avoid cold start latency
+    if llm_provider == "ollama":
+        print("\n======== OLLAMA MODEL INITIALIZATION ========")
+        print("Ollama models need to be loaded into memory on first use.")
+        print("This may take 30+ seconds depending on your hardware and model size.")
+        print("Please wait while the model is being loaded...")
+
+        # This call will load the model and measure initialization time
+        success = activities.warm_up_ollama()
+
+        if success:
+            print("===========================================================")
+            print("✅ Ollama model successfully pre-loaded and ready for requests!")
+            print("===========================================================\n")
+        else:
+            print("===========================================================")
+            print("⚠️ Ollama model pre-loading failed. The worker will continue,")
+            print("but the first actual request may experience a delay while")
+            print("the model is loaded on-demand.")
+            print("===========================================================\n")
+
+    print("Worker ready to process tasks!")
+
     # Run the worker
     with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
         worker = Worker(