refactor: move llm tests to separate integration and mocked files and centralize fixtures in conftest.py

src/photools3/llm_session.py

@@ -2,6 +2,7 @@ import json
 import re
 
 from openai import OpenAI
+import openai
 from openai.types.chat import ChatCompletion
 
 from photools3.models import (
@@ -14,7 +15,7 @@ from photools3.models import (
 from photools3.photo import Photo
 from photools3.settings import Settings
 
-settings = Settings()
+settings = Settings()  # type: ignore[reportCallIssue]
 
 
 class LlmSession:
@@ -32,6 +33,17 @@ class LlmSession:
             api_key=self.api_key,
         )
 
+    def client_chat_completion_create(self, request: LlmRequest, choice_no: int = 0):
+        response = self.client.chat.completions.create(**request.model_dump())
+        return response.choices[choice_no].message.content
+
+    def is_api_reachable(self) -> bool:
+        try:
+            _ = self.client.models.list().data
+            return True
+        except openai.APIConnectionError:
+            return False
+
     @property
     def models(self):
         if not self._models:
@@ -43,11 +55,33 @@ class LlmSession:
         prompt: str,
         photos: list[Photo],
         model_id=None,
-    ) -> ChatCompletion:
+    ):
         if not model_id:
             model_id = self.models[0].id
 
-        request = LlmRequest(
+        request = self._build_request(prompt, photos, model_id)
+        result_raw = self.client_chat_completion_create(request)
+
+        try:
+            result = json.loads(result_raw)
+        except json.JSONDecodeError as e:
+            try:
+                json_str = re.sub(
+                    r"^```json(.*)```$", r"\1", result_raw, flags=re.DOTALL
+                ).strip()
+                result = json.loads(json_str)
+            except json.JSONDecodeError:
+                raise e
+        return result
+
+    def _build_request(
+        self,
+        prompt: str,
+        photos: list[Photo],
+        model_id: str,
+    ) -> LlmRequest:
+        """Build a valid request that complies with openai API"""
+        return LlmRequest(
             model=model_id,
             temperature=self.temperature,
             messages=[
@@ -77,19 +111,3 @@ class LlmSession:
                 ),
             ],
         )
-        response = self.client.chat.completions.create(**request.model_dump())
-        result_raw = response.choices[0].message.content
-
-        # TODO: Add guard for non-JSON output
-
-        try:
-            result = json.loads(result_raw)
-        except json.JSONDecodeError as e:
-            try:
-                json_str = re.sub(
-                    r"^```json(.*)```$", r"\1", result_raw, flags=re.DOTALL
-                ).strip()
-                result = json.loads(json_str)
-            except json.JSONDecodeError:
-                raise e
-        return result

test/conftest.py

@@ -0,0 +1,17 @@
+import pytest
+from photools3.llm_session import LlmSession
+from photools3.photo import Photo
+from test.test_photo import PATHS_DNG_EXAMPLES
+
+@pytest.fixture
+def test_photo():
+    return Photo(PATHS_DNG_EXAMPLES[0])
+
+@pytest.fixture
+def llm_session():
+    return LlmSession(
+        system_prompt=(
+            "You are a helpful assistent to photographer. "
+            'Answer in JSON format: `{"caption": "...", "tags": ["tag1", "tag2", ...]}`'
+        )
+    )

test/test_llm_session.py

@@ -1,14 +0,0 @@
-from photools3.llm_session import LlmSession
-from photools3.photo import Photo
-from test.test_photo import PATHS_DNG_EXAMPLES
-
-llm_session = LlmSession(system_prompt="You are a helpful assistent to photographer")
-
-test_photo = Photo(PATHS_DNG_EXAMPLES[0])
-
-
-def test_llm_session():
-    result = llm_session.call(
-        prompt="keywords for the attaches picture", photos=[test_photo]
-    )
-    assert result

test/test_llm_session_integration.py

@@ -0,0 +1,10 @@
+import pytest
+from photools3.llm_session import LlmSession
+from photools3.photo import Photo
+
+@pytest.mark.skipif(not LlmSession(system_prompt="").is_api_reachable(), reason="API not available")
+def test_llm_session(llm_session, test_photo):
+    result = llm_session.call(
+        prompt="keywords for the attached picture", photos=[test_photo]
+    )
+    assert result

test/test_llm_session_mocked.py

@@ -0,0 +1,46 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from photools3.llm_session import LlmSession
+from photools3.photo import Photo
+from test.test_photo import PATHS_DNG_EXAMPLES
+
+def test_llm_session_mocked(test_photo):
+    # We want to mock OpenAI client's chat.completions.create
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        # Setup the mock response structure
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        
+        # Mock the models list to return a proper object with an ID string
+        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+        
+        # Create a response object: response.choices[0].message.content
+        mock_response = MagicMock()
+        mock_choice = MagicMock()
+        mock_message = MagicMock()
+        
+        mock_message.content = '{"caption": "A beautiful sunset", "tags": ["sunset", "nature"]}'
+        mock_choice.message = mock_message
+        mock_response.choices = [mock_choice]
+        
+        # Configure the client to return our mock response
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Create a new LlmSession instance that will use the mocked OpenAI client
+        mocked_session = LlmSession(system_prompt="You are a helper.")
+
+        # Run the test
+        result = mocked_session.call(
+            prompt="keywords for the attached picture", photos=[test_photo]
+        )
+
+        # Verify the results
+        assert result["caption"] == "A beautiful sunset"
+        assert "sunset" in result["tags"]
+        assert "nature" in result["tags"]
+        
+        # Verify that the mock was called
+        assert mock_client.chat.completions.create.called