Use OpenAI default model for OpenAI fallback

src/skillspector/llm_utils.py

@@ -33,13 +33,15 @@
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import BaseMessage
 
-from skillspector.constants import MODEL_CONFIG
 from skillspector.model_info import get_max_input_tokens, get_max_output_tokens
 from skillspector.providers import (
     create_chat_model,
+    get_metadata_provider,
     raise_no_llm_api_key_configured,
     resolve_chat_model_credentials,
+    resolve_provider_credentials,
 )
+from skillspector.providers.openai import OpenAIProvider
 
 
 def _resolve_llm_credentials() -> tuple[str, str | None]:
@@ -57,6 +59,18 @@ def _resolve_llm_credentials() -> tuple[str, str | None]:
     return creds
 
 
+def _resolve_default_chat_model() -> str:
+    """Return the default chat model for the endpoint that will be used."""
+    if resolve_provider_credentials() is not None:
+        return get_metadata_provider().resolve_model()
+
+    openai_provider = OpenAIProvider()
+    if openai_provider.resolve_credentials() is not None:
+        return openai_provider.resolve_model()
+
+    raise_no_llm_api_key_configured()
+
+
 def is_llm_available() -> tuple[bool, str | None]:
     """Return ``(available, error_message)`` describing LLM credential status."""
     try:
@@ -77,7 +91,7 @@ def get_chat_model(model: str | None = None) -> BaseChatModel:
     Raises:
         ValueError: when no API key is configured (see ``is_llm_available``).
     """
-    model = model or MODEL_CONFIG["default"]
+    model = model or _resolve_default_chat_model()
     return create_chat_model(
         model=model,
         max_tokens=get_max_output_tokens(model),

tests/unit/test_llm_utils.py

@@ -35,12 +35,15 @@
     is_llm_available,
 )
 from skillspector.providers import NO_LLM_API_KEY_MESSAGE, resolve_provider_credentials
+from skillspector.providers.nv_build import NvBuildProvider
+from skillspector.providers.openai import OpenAIProvider
 
 _LLM_ENV_VARS = (
     "ANTHROPIC_API_KEY",
     "OPENAI_API_KEY",
     "OPENAI_BASE_URL",
     "NVIDIA_INFERENCE_KEY",
+    "SKILLSPECTOR_MODEL",
     "SKILLSPECTOR_PROVIDER",
 )
 
@@ -178,3 +181,37 @@ def test_returns_false_with_message_when_no_credentials(self) -> None:
         ok, msg = is_llm_available()
         assert ok is False
         assert msg == NO_LLM_API_KEY_MESSAGE
+
+
+class TestGetChatModel:
+    def test_openai_fallback_uses_openai_default_model(
+        self, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("OPENAI_API_KEY", "sk-test-openai-only")
+
+        llm = get_chat_model()
+
+        assert _chat_model_name(llm) == OpenAIProvider.DEFAULT_MODEL
+
+    def test_explicit_model_still_overrides_openai_fallback(
+        self, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("OPENAI_API_KEY", "sk-test-openai-only")
+
+        llm = get_chat_model(model="custom/model")
+
+        assert _chat_model_name(llm) == "custom/model"
+
+    def test_provider_credentials_use_provider_default_model(
+        self, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        monkeypatch.setenv("NVIDIA_INFERENCE_KEY", "nvapi-test")
+        monkeypatch.setenv("OPENAI_API_KEY", "sk-test-openai")
+
+        llm = get_chat_model()
+
+        assert _chat_model_name(llm) == NvBuildProvider.DEFAULT_MODEL
+
+
+def _chat_model_name(llm: object) -> str:
+    return str(getattr(llm, "model_name", None) or getattr(llm, "model", None))