feat: add cli support for ollama users who run their model locally

cli/main.py

@@ -479,7 +479,8 @@ def get_user_selections():
     )
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
     selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
-
+    selected_embedding_model = select_embedding_agent(selected_llm_provider)
+    
     return {
         "ticker": selected_ticker,
         "analysis_date": analysis_date,
@@ -489,6 +490,7 @@ def get_user_selections():
         "backend_url": backend_url,
         "shallow_thinker": selected_shallow_thinker,
         "deep_thinker": selected_deep_thinker,
+        "embedding_model": selected_embedding_model,
     }
 
 
@@ -741,6 +743,7 @@ def run_analysis():
     config["max_risk_discuss_rounds"] = selections["research_depth"]
     config["quick_think_llm"] = selections["shallow_thinker"]
     config["deep_think_llm"] = selections["deep_thinker"]
+    config["embedding_model"] = selections["embedding_model"]
     config["backend_url"] = selections["backend_url"]
     config["llm_provider"] = selections["llm_provider"].lower()
 

cli/utils.py

@@ -1,5 +1,5 @@
 import questionary
-from typing import List, Optional, Tuple, Dict
+from typing import List, Optional, Tuple, Dict, Sequence
 
 from cli.models import AnalystType
 
@@ -10,6 +10,55 @@ ANALYST_ORDER = [
     ("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
 ]
 
+def _ask_custom_model(label: str) -> str:
+    """Prompt the user to type an arbitrary model name."""
+    model_name = questionary.text(
+        f"Enter the exact Ollama model name for {label}:",
+        validate=lambda x: len(x.strip()) > 0 or "Model name cannot be empty.",
+        style=questionary.Style([("text", "fg:green")]),
+    ).ask()
+    if not model_name:
+        console.print(f"\n[red]No model name provided. Exiting...[/red]")
+        exit(1)
+    return model_name
+
+def _select_llm(
+    provider: str,
+    label: str,
+    options: Sequence[Tuple[str, str]],
+) -> str:
+    """
+    Generic interactive selector that optionally offers a 'custom' entry
+    for Ollama users.
+    """
+    opts = list(options)
+    if provider.lower() == "ollama":
+        opts.append(("Custom model (type manually)", "__CUSTOM__"))
+
+    choice = questionary.select(
+        f"Select Your [{label}] LLM Engine:",
+        choices=[questionary.Choice(d, v) for d, v in opts],
+        style=questionary.Style(
+            [
+                ("selected", "fg:magenta noinherit"),
+                ("highlighted", "fg:magenta noinherit"),
+                ("pointer", "fg:magenta noinherit"),
+            ]
+        ),
+    ).ask()
+
+    if choice is None:
+        console.print(f"\n[red]No {label.lower()} engine selected. Exiting...[/red]")
+        exit(1)
+
+    if choice == "__CUSTOM__":
+        # ask for arbitrary name
+        model_name = _ask_custom_model(label)
+        if model_name is None:
+            console.print("\n[red]No model name provided. Exiting...[/red]")
+            exit(1)
+        return model_name.strip()
+    return choice
 
 def get_ticker() -> str:
     """Prompt the user to enter a ticker symbol."""
@@ -154,30 +203,7 @@ def select_shallow_thinking_agent(provider) -> str:
             ("llama3.2 local", "llama3.2"),
         ]
     }
-
-    choice = questionary.select(
-        "Select Your [Quick-Thinking LLM Engine]:",
-        choices=[
-            questionary.Choice(display, value=value)
-            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
-        ],
-        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
-        style=questionary.Style(
-            [
-                ("selected", "fg:magenta noinherit"),
-                ("highlighted", "fg:magenta noinherit"),
-                ("pointer", "fg:magenta noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if choice is None:
-        console.print(
-            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
-        )
-        exit(1)
-
-    return choice
+    return _select_llm(provider, "Quick-Thinking LLM Engine", SHALLOW_AGENT_OPTIONS[provider.lower()])
 
 
 def select_deep_thinking_agent(provider) -> str:
@@ -217,27 +243,22 @@ def select_deep_thinking_agent(provider) -> str:
         ]
     }
     
-    choice = questionary.select(
-        "Select Your [Deep-Thinking LLM Engine]:",
-        choices=[
-            questionary.Choice(display, value=value)
-            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
+    return _select_llm(provider, "Deep-Thinking LLM Engine", DEEP_AGENT_OPTIONS[provider.lower()])
+
+def select_embedding_agent(provider) -> str:
+    """Select embedding llm engine using an interactive selection."""
+
+    # Define deep thinking llm engine options with their corresponding model names
+    EMBEDDING_AGENT_OPTIONS = {
+        "openai": [
+            ("GPT", "text-embedding-3-small"),
         ],
-        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
-        style=questionary.Style(
-            [
-                ("selected", "fg:magenta noinherit"),
-                ("highlighted", "fg:magenta noinherit"),
-                ("pointer", "fg:magenta noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if choice is None:
-        console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
-        exit(1)
-
-    return choice
+        "ollama": [
+            
+        ]
+    }
+    
+    return _select_llm(provider, "Embedding LLM Engine", EMBEDDING_AGENT_OPTIONS[provider.lower()])
 
 def select_llm_provider() -> tuple[str, str]:
     """Select the OpenAI api url using interactive selection."""
@@ -247,7 +268,7 @@ def select_llm_provider() -> tuple[str, str]:
         ("Anthropic", "https://api.anthropic.com/"),
         ("Google", "https://generativelanguage.googleapis.com/v1"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
-        ("Ollama", "http://localhost:11434/v1"),        
+        ("Ollama", "http://localhost:11434"),        
     ]
     
     choice = questionary.select(