add vllm support

cli/main.py

@@ -544,26 +544,17 @@ def get_user_selections():
     )
     selected_llm_provider, backend_url = select_llm_provider()
     
-    # Step 6: Thinking agents
+    # Step 6: Provider-specific thinking configuration
-    console.print(
-        create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
-        )
-    )
-    selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
-    selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
-
-    # Step 7: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
     vllm_api_base = None
     vllm_api_key = None
-
+    
     provider_lower = selected_llm_provider.lower()
-    if provider_lower == "google":
+    if provider_lower == "Google":
         console.print(
             create_question_box(
-                "Step 7: Thinking Mode",
+                "Step 6: Thinking Mode",
                 "Configure Gemini thinking mode"
             )
         )
@@ -571,7 +562,7 @@ def get_user_selections():
     elif provider_lower == "openai":
         console.print(
             create_question_box(
-                "Step 7: Reasoning Effort",
+                "Step 6: Reasoning Effort",
                 "Configure OpenAI reasoning effort level"
             )
         )
@@ -579,11 +570,51 @@ def get_user_selections():
     elif provider_lower == "vllm":
         console.print(
             create_question_box(
-                "Step 7 : vLLM Configuration",
+                "Step 6 : vLLM Configuration",
                 "Configure vLLM API configuration"
             )
         )
         vllm_api_base, vllm_api_key = ask_vllm_config()
+        
+    # Step 7: Thinking agents
+    console.print(
+        create_question_box(
+            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
+        )
+    )
+    
+    # If vLLM is selected, fetch model name from /v1/models endpoint
+    if provider_lower == "vllm" and vllm_api_base:
+        try:
+            import requests
+            base_url = vllm_api_base.rstrip('/')
+            headers = {}
+            if vllm_api_key:
+                headers["Authorization"] = f"Bearer {vllm_api_key}"
+            
+            response = requests.get(f"{base_url}/v1/models", headers=headers, timeout=5)
+            if response.status_code != 200:
+                raise Exception(f"Failed to fetch models from vLLM: status code {response.status_code}")
+            
+            data = response.json()
+            if 'data' not in data or len(data['data']) == 0:
+                raise Exception("No models returned from vLLM API")
+            
+            model_name = data['data'][0].get('id', 'unknown')
+            console.print(f"[green]✓ Fetched model from vLLM:[/green] {model_name}")
+            selected_shallow_thinker = model_name
+            selected_deep_thinker = model_name
+            
+        except requests.exceptions.ConnectionError:
+            console.print(f"[red]Error: Cannot connect to vLLM at {vllm_api_base}[/red]")
+            raise
+        except Exception as e:
+            console.print(f"[red]Error: {e}[/red]")
+            raise
+    else:
+        # For other providers, use normal selection
+        selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
+        selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
 
     return {
         "ticker": selected_ticker,