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Added option to select own locally running Ollama model when running python -m cli.main #221

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Added option to select own locally running Ollama model when running python -m cli.main #221

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24 changes: 24 additions & 0 deletions cli/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,6 +152,7 @@ def select_shallow_thinking_agent(provider) -> str:
"ollama": [
("llama3.1 local", "llama3.1"),
("llama3.2 local", "llama3.2"),
("Custom (enter model name)", "__custom__"),
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@gemini-code-assist gemini-code-assist bot Sep 10, 2025

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medium

The string "__custom__" is a magic value used for the choice's value and for comparison later. To improve readability and maintainability, it's best to define this as a constant at the module level (e.g., CUSTOM_OLLAMA_MODEL_CHOICE = "__custom__") and use that constant in both places. This would also apply to its usage for the deep thinking agent.

]
}

Expand All @@ -177,6 +178,17 @@ def select_shallow_thinking_agent(provider) -> str:
)
exit(1)

# If custom is selected, prompt for the model name to use with Ollama
if choice == "__custom__":
custom_model = questionary.text(
"Enter your Ollama model name (e.g., mistral-nemo:latest):",
validate=lambda x: len(x.strip()) > 0 or "Please enter a valid model name.",
).ask()
if not custom_model:
console.print("\n[red]No model name provided. Exiting...[/red]")
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high

The console object is used here but it is not defined or imported in cli/utils.py. It appears to be defined as a global in cli/main.py, which will likely cause a NameError at runtime because this module doesn't have access to main.py's globals. This is a critical correctness issue. This anti-pattern is also present in the new code in select_deep_thinking_agent (line 260). The console object should be made available to this module properly, for instance by defining it in a shared location and importing it where needed.

exit(1)
return custom_model.strip()
Comment on lines +181 to +190
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medium

This block of code for handling a custom model selection is nearly identical to the one in select_deep_thinking_agent (lines 253-262). To improve maintainability and adhere to the DRY (Don't Repeat Yourself) principle, this logic should be extracted into a private helper function. The example model name could be passed as an argument to this new function.


return choice


Expand Down Expand Up @@ -214,6 +226,7 @@ def select_deep_thinking_agent(provider) -> str:
"ollama": [
("llama3.1 local", "llama3.1"),
("qwen3", "qwen3"),
("Custom (enter model name)", "__custom__"),
]
}

Expand All @@ -237,6 +250,17 @@ def select_deep_thinking_agent(provider) -> str:
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)

# If custom is selected, prompt for the model name to use with Ollama
if choice == "__custom__":
custom_model = questionary.text(
"Enter your Ollama model name (e.g., llama3.1:latest):",
validate=lambda x: len(x.strip()) > 0 or "Please enter a valid model name.",
).ask()
if not custom_model:
console.print("\n[red]No model name provided. Exiting...[/red]")
exit(1)
return custom_model.strip()

return choice

def select_llm_provider() -> tuple[str, str]:
Expand Down