feat(#70): support gemini 2.5 stable models (#71)

closes #70

Author: scastlara

src/lgtm_ai/ai/schemas.py

@@ -39,9 +39,11 @@
     "gemini-2.0-flash-lite-preview-02-05",
     "gemini-2.0-pro-exp-02-05",
     "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-pro",
     "gemini-2.5-pro-exp-03-25",
     "gemini-2.5-pro-preview-03-25",
     "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-flash",
     "gemini-2.5-flash-preview-05-20",
     "gemini-2.5-pro-preview-06-05",
 ]

src/lgtm_ai/ai/utils.py

@@ -26,6 +26,16 @@ def select_latest_gemini_model(matches: list[SupportedGeminiModel]) -> Supported
     if len(matches) == 1:
         return matches[0]
 
+    # If one of them is not a `preview` or `exp` model, select it
+    # This is because out of several possible models given a wildcard, we assume preference for stable models.
+    # If the user wanted a preview or experimental model, they could have specified it in the wildcard itself:
+    # e.g.: `gemini-2.5-flash*` will select the latest stable, while `gemini-2.5-flash-p*` will select the latest preview.
+    non_preview_matches = [model for model in matches if all(word not in model for word in ("preview", "exp"))]
+    if non_preview_matches:
+        if len(non_preview_matches) > 1:
+            raise InvalidGeminiWildcard(matches)
+        return non_preview_matches[0]
+
     model_to_date = {model: model.split("-")[-2:] for model in matches}
 
     def _looks_like_date(date: list[str]) -> bool:

tests/ai/test_utils.py

@@ -51,6 +51,8 @@ def test_get_ai_model(model: str, model_url: str | None, ai_api_key: str, expect
         ("gemini-2.5-flash-*-*", None, pytest.raises(InvalidModelWildCard)),
         # Wildcards in the middle of the model name are not allowed
         ("gemini-*-pro-preview-06-05", None, pytest.raises(InvalidModelWildCard)),
+        # Wildcards that match preview, experimental and stable models must select the stable one
+        ("gemini-2.5-pro*", "gemini-2.5-pro", does_not_raise()),
     ],
 )
 def test_get_ai_model_with_wildcard(model: str, expected_model_name: str, expectation: Any) -> None: