Ticket AI-012 #23
Conversation
WalkthroughThis update introduces significant improvements and modernization across the AI backend project. Key changes include environment variable handling and validation updates in the configuration, expanded and robust API client logic for sports data, and the addition of comprehensive test suites for dependencies, OpenAI integration, and API client behavior. Build, lint, and test scripts are simplified for streamlined developer workflows, and a Makefile is added for unified project management. Changes
Sequence Diagram(s)sequenceDiagram
participant Developer
participant Makefile
participant Scripts
participant Tests
participant APIClient as APIFootballClient
participant ExternalAPI as API-Football
Developer->>Makefile: make test
Makefile->>Scripts: scripts/run-tests.sh
Scripts->>Tests: pytest tests/
Tests->>APIClient: get_fixtures(...)
APIClient->>ExternalAPI: HTTP GET /fixtures
ExternalAPI-->>APIClient: JSON response
APIClient-->>Tests: fixture list
Tests-->>Scripts: test results
Scripts-->>Makefile: completion message
Makefile-->>Developer: test summary
sequenceDiagram
participant APIClient as APIFootballClient
participant Env as dotenv
participant Logger
participant HTTPClient as aiohttp.ClientSession
participant ExternalAPI as API-Football
APIClient->>Env: load API key
APIClient->>Logger: log configuration
APIClient->>HTTPClient: prepare headers
APIClient->>APIClient: _build_fixture_payload(...)
APIClient->>Logger: log payload
APIClient->>HTTPClient: GET /fixtures with payload
HTTPClient->>ExternalAPI: HTTP GET
ExternalAPI-->>HTTPClient: JSON response
HTTPClient-->>APIClient: response
APIClient->>Logger: log result
APIClient-->>Caller: fixture list
Estimated code review effort🎯 4 (Complex) | ⏱️ ~40 minutes Poem
Note ⚡️ Unit Test Generation is now available in beta!Learn more here, or try it out under "Finishing Touches" below. ✨ Finishing Touches
🧪 Generate unit tests
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (3)
ai-backend/test_openai.py (2)
16-16: Inconsistent placeholder value check.The placeholder check
"your_openai_api_key_here"doesn't match the placeholder values in the settings validation ("your-openai-api-key","sk-..."). Consider aligning these for consistency.- if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") == "your_openai_api_key_here": + if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") in ["your-openai-api-key", "sk-..."]:
14-37: Consider adding type hints and better error handling.The test function would benefit from type annotations and more specific error handling to distinguish between different types of API failures.
-def test_openai_connection(): +def test_openai_connection() -> None: """Test basic OpenAI API connection""" - if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") == "your_openai_api_key_here": + api_key = os.getenv("OPENAI_API_KEY") + if not api_key or api_key in ["your-openai-api-key", "sk-..."]: print("⚠️ OpenAI API key not set. Skipping connection test.") return try: # Test with a simple completion response = client.chat.completions.create( - model="gpt-4.1-nano", + model="gpt-4-turbo", messages=[ {"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"} ], max_tokens=50 ) print("✅ OpenAI API connection successful!") print(f"Response: {response.choices[0].message.content}") - except Exception as e: + except openai.AuthenticationError: + print("❌ OpenAI API authentication failed: Invalid API key") + except openai.RateLimitError: + print("❌ OpenAI API rate limit exceeded") + except Exception as e: print(f"❌ OpenAI API connection failed: {e}")ai-backend/test_environment.py (1)
17-22: Fix misleading success message.The success message incorrectly refers to "OpenAI Agents package" when importing
ResearchAgentfrom the localagents.researchermodule.- print("✅ OpenAI Agents package imported successfully") + print("✅ ResearchAgent imported successfully")
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
ai-backend/config/settings.py(4 hunks)ai-backend/test_environment.py(1 hunks)ai-backend/test_openai.py(1 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (1)
ai-backend/test_environment.py (1)
ai-backend/agents/researcher.py (1)
ResearchAgent(16-72)
🪛 GitHub Check: Codacy Static Code Analysis
ai-backend/test_environment.py
[warning] 11-11: ai-backend/test_environment.py#L11
Unused import openai
[warning] 18-18: ai-backend/test_environment.py#L18
Unused ResearchAgent imported from agents.researcher
[warning] 25-25: ai-backend/test_environment.py#L25
Unused import fastapi
[warning] 32-32: ai-backend/test_environment.py#L32
Unused BaseModel imported from pydantic
[warning] 39-39: ai-backend/test_environment.py#L39
Unused create_client imported from supabase
[warning] 46-46: ai-backend/test_environment.py#L46
Unused import aiohttp
[warning] 53-53: ai-backend/test_environment.py#L53
Unused load_dotenv imported from dotenv
[warning] 60-60: ai-backend/test_environment.py#L60
Unused import structlog
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (3)
- GitHub Check: Codacy Static Code Analysis
- GitHub Check: Codacy Static Code Analysis
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (5)
ai-backend/config/settings.py (4)
11-12: LGTM! Modern Pydantic v2 patterns correctly implemented.The migration from
@validatorto@field_validatorand the use ofSettingsConfigDictaligns with current Pydantic best practices.
59-100: LGTM! Validator modernization correctly implemented.All validators have been properly updated to use
@field_validatorwith@classmethoddecorators. The validation logic remains unchanged, preserving existing behavior.
118-120: LGTM! Configuration modernization follows Pydantic v2 patterns.The replacement of the inner
Configclass withmodel_configusingSettingsConfigDictis the correct approach for Pydantic v2.
24-31: Environment variable naming consistency verified across the codebaseAll references to environment variables have been updated to uppercase, and no lingering lowercase names remain.
Please ensure your.envfiles define the following keys accordingly:
- OPENAI_API_KEY
- SUPABASE_URL
- SUPABASE_SERVICE_ROLE_KEY
- RAPIDAPI_KEY
ai-backend/test_environment.py (1)
10-65: LGTM! Effective dependency verification approach.This script correctly tests the importability of all critical dependencies. The static analysis warnings about "unused imports" are false positives since testing imports is the explicit purpose of this script.
| try: | ||
| # Test with a simple completion | ||
| response = client.chat.completions.create( | ||
| model="gpt-4.1-nano", |
There was a problem hiding this comment.
Fix the invalid OpenAI model name.
The model "gpt-4.1-nano" is not a valid OpenAI model. Use a supported model name like "gpt-4-turbo" or "gpt-3.5-turbo".
- model="gpt-4.1-nano",
+ model="gpt-4-turbo",📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| model="gpt-4.1-nano", | |
| model="gpt-4-turbo", |
🤖 Prompt for AI Agents
In ai-backend/test_openai.py at line 23, the model name "gpt-4.1-nano" is
invalid. Replace it with a valid OpenAI model name such as "gpt-4-turbo" or
"gpt-3.5-turbo" to ensure compatibility with the OpenAI API.
There was a problem hiding this comment.
Actionable comments posted: 13
🔭 Outside diff range comments (2)
ai-backend/tests/test_quality_tools.py (1)
1-67: Mismatch between filename and content - this appears to be implementation code in a test file.The file
test_quality_tools.pycontains implementation code (FootballDataProcessorclass) rather than actual tests. Test files should containtest_*functions that verify the behavior of code located elsewhere.Consider either:
- Moving
FootballDataProcessorto a proper module (e.g.,ai-backend/tools/data_processing.py)- Adding actual test functions to verify the
FootballDataProcessorbehaviorExample test structure:
import pytest from your_module import FootballDataProcessor def test_process_game_data(): processor = FootballDataProcessor("Premier League") result = processor.process_game_data("Arsenal", "Chelsea", "2-1") assert result["home_team"] == "Arsenal" assert result["away_team"] == "Chelsea" assert result["score"] == "2-1" assert result["league"] == "Premier League" @pytest.mark.asyncio async def test_fetch_recent_games(): processor = FootballDataProcessor("Premier League") games = await processor.fetch_recent_games(limit=1) assert len(games) == 1 assert games[0]["league"] == "Premier League"ai-backend/tests/test_fixtures.py (1)
1-145: Add missing test coverage for fixture_id and error scenarios.The test suite is missing coverage for:
- Testing with
fixture_idparameter- Testing when API key is missing (should return empty list)
- Testing API error handling (HTTP errors, network errors)
Would you like me to generate the additional test cases to improve coverage?
🧹 Nitpick comments (7)
scripts/run-tests.sh (1)
4-12: Optimize to avoid running tests twice.The script runs pytest twice - once for tests and once for coverage. This is inefficient and could cause inconsistent results if tests are flaky.
echo "🧪 Running test suite..." -echo "🔬 Unit tests..." -pytest tests/ -v - -echo "📊 Coverage report..." -pytest --cov=. --cov-report=term-missing +echo "🔬 Running tests with coverage..." +pytest tests/ -v --cov=. --cov-report=term-missing --cov-report=html:htmlcovMakefile (1)
6-9: Consider adding error handling for dependency installation.The install target could benefit from error handling to ensure dependencies are installed successfully before setting up pre-commit hooks.
install: ## Install dependencies - pip install -r requirements.txt - pip install -r requirements-dev.txt - pre-commit install + pip install -r requirements.txt || exit 1 + pip install -r requirements-dev.txt || exit 1 + pre-commit install || exit 1ai-backend/tests/test_quality_tools.py (1)
50-53: The limit parameter is not effectively used.The
limitparameter slices a hardcoded 2-item list, so it only works for limits of 0, 1, or 2. For a more realistic simulation, consider generating more sample data or making the limit more meaningful.return [ self.process_game_data("Arsenal", "Chelsea", "2-1"), self.process_game_data("Liverpool", "Manchester City", "1-3"), -][:limit] + self.process_game_data("Tottenham", "Manchester United", "0-2"), + self.process_game_data("Brighton", "Newcastle", "1-1"), + self.process_game_data("Wolves", "Everton", "2-0"), +][:limit]ai-backend/tests/test_fixtures.py (1)
1-4: Consider addingaiohttpto imports for type annotations.Since the tests are mocking
aiohttp.ClientSession, importingaiohttpwould make the mocking more explicit and help with type checking.import pytest +import aiohttp from tools.sports_apis import APIFootballClientai-backend/tools/test_api_auth.py (2)
10-11: Remove or uncomment the security import.The commented import suggests these security functions might be needed. Either use them or remove the comment.
-# from utils.security import sanitize_log_input, sanitize_multiple_log_inputs +from utils.security import sanitize_log_input, sanitize_multiple_log_inputs
46-54: Simplify session cleanup logic.The null check and explicit return statements are unnecessary. The method will return None by default.
async def __aexit__( self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: Any, ) -> None: if self.session: await self.session.close() - self.session = None - logger.info("API session closed.") - return - - logger.error("API session not initialized.") - returnai-backend/tools/sports_apis.py (1)
35-42: Consider fail-fast approach for missing API key.Currently, the client silently creates empty headers when API key is missing. Consider raising an exception in
__init__to fail fast.def __init__(self, api_key: str | None = None): self.api_key = api_key or os.getenv("RAPIDAPI_KEY") self.base_url = "https://api-football-v1.p.rapidapi.com/v3" if self.api_key: self.headers = { "X-RapidAPI-Key": self.api_key, "X-RapidAPI-Host": "api-football-v1.p.rapidapi.com", } else: - self.headers = {} + raise ValueError("API key is required. Set RAPIDAPI_KEY environment variable or pass api_key parameter.")
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (17)
Makefile(1 hunks)ai-backend/.pre-commit-config.yaml(1 hunks)ai-backend/agents/editor.py(1 hunks)ai-backend/agents/researcher.py(2 hunks)ai-backend/config/settings.py(5 hunks)ai-backend/main.py(3 hunks)ai-backend/pytest.ini(1 hunks)ai-backend/tests/test_environment.py(1 hunks)ai-backend/tests/test_fixtures.py(1 hunks)ai-backend/tests/test_openai.py(1 hunks)ai-backend/tests/test_quality_tools.py(1 hunks)ai-backend/tools/sports_apis.py(3 hunks)ai-backend/tools/test_api_auth.py(1 hunks)scripts/lint-fix.sh(1 hunks)scripts/run-tests.sh(1 hunks)scripts/seed-data.py(10 hunks)scripts/type-check.sh(1 hunks)
✅ Files skipped from review due to trivial changes (7)
- ai-backend/.pre-commit-config.yaml
- ai-backend/main.py
- ai-backend/tests/test_environment.py
- ai-backend/agents/editor.py
- scripts/type-check.sh
- ai-backend/agents/researcher.py
- scripts/seed-data.py
🚧 Files skipped from review as they are similar to previous changes (1)
- ai-backend/config/settings.py
🧰 Additional context used
🧬 Code Graph Analysis (2)
ai-backend/tests/test_fixtures.py (1)
ai-backend/tools/sports_apis.py (2)
APIFootballClient(23-210)get_fixtures(58-92)
ai-backend/tools/sports_apis.py (2)
ai-backend/utils/security.py (2)
sanitize_log_input(7-26)sanitize_multiple_log_inputs(29-38)ai-backend/tests/test_fixtures.py (12)
get(24-25)get(60-61)get(88-89)get(126-127)raise_for_status(14-15)raise_for_status(50-51)raise_for_status(78-79)raise_for_status(116-117)json(11-12)json(47-48)json(75-76)json(103-114)
🪛 checkmake (0.2.2)
Makefile
[warning] 1-1: Missing required phony target "all"
(minphony)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (9)
ai-backend/pytest.ini (2)
11-14: LGTM! Excellent coverage configuration.The coverage settings are well-configured with appropriate reporting formats and a reasonable 80% threshold. The HTML report will be particularly useful for identifying uncovered code.
15-19: Well-defined test markers for better test organization.The test markers are clearly defined and will help with selective test execution. The example for deselecting slow tests is particularly helpful.
Makefile (3)
1-1: Static analysis false positive - "all" target exists.The static analysis tool incorrectly flagged a missing "all" target. The target is clearly defined on line 43 and properly included in the .PHONY declaration.
3-4: Excellent help system implementation.The help target uses a sophisticated awk script to automatically generate formatted help text from target comments. This is a best practice for Makefile documentation.
43-47: Well-designed "all" target for comprehensive quality checks.The "all" target properly sequences formatting, linting, type checking, and testing - a logical workflow for code quality assurance.
ai-backend/tests/test_quality_tools.py (1)
14-36: Good implementation quality with proper type hints and documentation.The
process_game_datamethod is well-implemented with:
- Clear type hints
- Comprehensive docstring with Args/Returns
- Input sanitization with
.strip()- Proper optional parameter handling
ai-backend/tests/test_fixtures.py (2)
35-40: LGTM! Test correctly validates the season requirement.This test properly verifies that the method returns an empty list when league is provided without season, which aligns with the implementation's validation logic.
140-145: LGTM! Clean test for edge case.This test properly verifies that calling
get_fixtureswithout parameters returns an empty list, which is the expected behavior.ai-backend/tools/test_api_auth.py (1)
75-77: Add session initialization check.The method assumes
self.sessionexists but it might be None if used outside the context manager.async def _fetch_data(self, url, payload) -> list[dict[str, Any]]: try: + if not self.session: + logger.error("Session not initialized. Use async context manager.") + return [] async with self.session.get(
| async def test_get_fixtures_by_league(monkeypatch): | ||
| class MockResponse: | ||
| status = 200 | ||
|
|
||
| async def json(self): | ||
| return {"response": [{"fixture": 1, "league": 39, "season": 2025}]} | ||
|
|
||
| def raise_for_status(self): | ||
| pass | ||
|
|
||
| async def __aenter__(self): | ||
| return self | ||
|
|
||
| async def __aexit__(self, exc_type, exc, tb): | ||
| pass | ||
|
|
||
| class MockSession: | ||
| def get(self, *args, **kwargs): | ||
| return MockResponse() | ||
|
|
||
| client = APIFootballClient(api_key="test") | ||
| client.session = MockSession() | ||
| result = await client.get_fixtures(league=39, season=2025) | ||
| assert isinstance(result, list) | ||
| assert result[0]["league"] == 39 | ||
| assert result[0]["season"] == 2025 | ||
|
|
There was a problem hiding this comment.
Test bypasses proper session initialization.
The test directly assigns client.session = MockSession() which bypasses the proper async context manager initialization. This doesn't accurately test the real usage pattern of the client.
Consider using the async context manager properly:
- client = APIFootballClient(api_key="test")
- client.session = MockSession()
- result = await client.get_fixtures(league=39, season=2025)
+ async with APIFootballClient(api_key="test") as client:
+ client.session = MockSession() # Still need to mock, but within context
+ result = await client.get_fixtures(league=39, season=2025)Also, the monkeypatch parameter is unused and can be removed from all test functions.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| async def test_get_fixtures_by_league(monkeypatch): | |
| class MockResponse: | |
| status = 200 | |
| async def json(self): | |
| return {"response": [{"fixture": 1, "league": 39, "season": 2025}]} | |
| def raise_for_status(self): | |
| pass | |
| async def __aenter__(self): | |
| return self | |
| async def __aexit__(self, exc_type, exc, tb): | |
| pass | |
| class MockSession: | |
| def get(self, *args, **kwargs): | |
| return MockResponse() | |
| client = APIFootballClient(api_key="test") | |
| client.session = MockSession() | |
| result = await client.get_fixtures(league=39, season=2025) | |
| assert isinstance(result, list) | |
| assert result[0]["league"] == 39 | |
| assert result[0]["season"] == 2025 | |
| async def test_get_fixtures_by_league(monkeypatch): | |
| class MockResponse: | |
| status = 200 | |
| async def json(self): | |
| return {"response": [{"fixture": 1, "league": 39, "season": 2025}]} | |
| def raise_for_status(self): | |
| pass | |
| async def __aenter__(self): | |
| return self | |
| async def __aexit__(self, exc_type, exc, tb): | |
| pass | |
| class MockSession: | |
| def get(self, *args, **kwargs): | |
| return MockResponse() | |
| async with APIFootballClient(api_key="test") as client: | |
| client.session = MockSession() # Still need to mock, but within context | |
| result = await client.get_fixtures(league=39, season=2025) | |
| assert isinstance(result, list) | |
| assert result[0]["league"] == 39 | |
| assert result[0]["season"] == 2025 |
🤖 Prompt for AI Agents
In ai-backend/tests/test_fixtures.py around lines 7 to 33, the test bypasses
proper async session initialization by directly assigning client.session to a
mock. Refactor the test to mock the session using an async context manager that
mimics the real client's usage pattern, ensuring the session is created and used
within an async with block. Also, remove the unused monkeypatch parameter from
the test function signature.
| class MockResponse: | ||
| status = 200 | ||
|
|
||
| async def json(self): | ||
| return {"response": [{"fixture": 1, "league": 39, "season": 2025}]} | ||
|
|
||
| def raise_for_status(self): | ||
| pass | ||
|
|
||
| async def __aenter__(self): | ||
| return self | ||
|
|
||
| async def __aexit__(self, exc_type, exc, tb): | ||
| pass | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Extract duplicate mock classes to reduce code duplication.
The MockResponse and MockSession classes are duplicated across multiple tests with only minor variations in the JSON response. This violates the DRY principle.
Create reusable mock fixtures at the module level:
class MockResponse:
def __init__(self, json_data):
self.status = 200
self._json_data = json_data
async def json(self):
return self._json_data
def raise_for_status(self):
pass
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc, tb):
pass
class MockSession:
def __init__(self, response_data):
self.response_data = response_data
def get(self, *args, **kwargs):
return MockResponse(self.response_data)Then update tests to use these shared mocks:
async def test_get_fixtures_by_date():
response_data = {"response": [{"fixture": 2, "date": "2025-07-10"}]}
async with APIFootballClient(api_key="test") as client:
client.session = MockSession(response_data)
result = await client.get_fixtures(date="2025-07-10")
assert isinstance(result, list)
assert result[0]["date"] == "2025-07-10"Also applies to: 44-58, 72-86, 100-124
🤖 Prompt for AI Agents
In ai-backend/tests/test_fixtures.py around lines 8 to 22, 44 to 58, 72 to 86,
and 100 to 124, the MockResponse and MockSession classes are duplicated with
minor JSON response differences, violating the DRY principle. Refactor by
creating reusable MockResponse and MockSession classes at the module level that
accept JSON data as initialization parameters. Then update the tests to
instantiate these mocks with the specific JSON data needed for each test,
replacing the duplicated classes with these shared fixtures.
| load_dotenv() | ||
|
|
||
| # Set up OpenAI client | ||
| client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
There was a problem hiding this comment.
Move client initialization inside the test function to avoid initialization errors.
Global client initialization will fail if the API key is not available, causing import errors. Move the client creation inside the test function after the API key validation.
-# Set up OpenAI client
-client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In ai-backend/tests/test_openai.py at line 12, the OpenAI client is initialized
globally which can cause import errors if the API key is missing. Move the
client initialization inside the test function, after verifying the API key is
present, to prevent initialization errors during import.
| def test_openai_connection() -> None: | ||
| """Test basic OpenAI API connection.""" | ||
| if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") == "your_openai_api_key_here": | ||
| print("⚠️ OpenAI API key not set. Skipping connection test.") | ||
| return | ||
|
|
||
| try: | ||
| # Test with a simple completion | ||
| response = client.chat.completions.create( | ||
| model="gpt-4.1-nano", | ||
| messages=[{"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"}], | ||
| max_tokens=50, | ||
| ) | ||
|
|
||
| print("✅ OpenAI API connection successful!") | ||
| print(f"Response: {response.choices[0].message.content}") | ||
|
|
||
| except Exception as e: | ||
| print(f"❌ OpenAI API connection failed: {e}") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Convert to proper pytest test with assertions instead of print statements.
This function uses print statements instead of proper test assertions, making it unsuitable as a pytest test. Consider converting to a proper test structure.
+import pytest
+
def test_openai_connection() -> None:
"""Test basic OpenAI API connection."""
- if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") == "your_openai_api_key_here":
- print("⚠️ OpenAI API key not set. Skipping connection test.")
- return
+ api_key = os.getenv("OPENAI_API_KEY")
+ if not api_key or api_key == "your_openai_api_key_here":
+ pytest.skip("OpenAI API key not set")
+
+ client = openai.OpenAI(api_key=api_key)
- try:
- # Test with a simple completion
- response = client.chat.completions.create(
- model="gpt-4.1-nano",
- messages=[{"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"}],
- max_tokens=50,
- )
-
- print("✅ OpenAI API connection successful!")
- print(f"Response: {response.choices[0].message.content}")
-
- except Exception as e:
- print(f"❌ OpenAI API connection failed: {e}")
+ # Test with a simple completion
+ response = client.chat.completions.create(
+ model="gpt-4", # Use valid model name
+ messages=[{"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"}],
+ max_tokens=50,
+ )
+
+ assert response.choices
+ assert response.choices[0].message.content
+ assert "Hello from Sport Scribe AI!" in response.choices[0].message.content📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def test_openai_connection() -> None: | |
| """Test basic OpenAI API connection.""" | |
| if not os.getenv("OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") == "your_openai_api_key_here": | |
| print("⚠️ OpenAI API key not set. Skipping connection test.") | |
| return | |
| try: | |
| # Test with a simple completion | |
| response = client.chat.completions.create( | |
| model="gpt-4.1-nano", | |
| messages=[{"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"}], | |
| max_tokens=50, | |
| ) | |
| print("✅ OpenAI API connection successful!") | |
| print(f"Response: {response.choices[0].message.content}") | |
| except Exception as e: | |
| print(f"❌ OpenAI API connection failed: {e}") | |
| import pytest | |
| def test_openai_connection() -> None: | |
| """Test basic OpenAI API connection.""" | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| if not api_key or api_key == "your_openai_api_key_here": | |
| pytest.skip("OpenAI API key not set") | |
| client = openai.OpenAI(api_key=api_key) | |
| # Test with a simple completion | |
| response = client.chat.completions.create( | |
| model="gpt-4", # Use valid model name | |
| messages=[{"role": "user", "content": "Say 'Hello from Sport Scribe AI!'"}], | |
| max_tokens=50, | |
| ) | |
| assert response.choices | |
| assert response.choices[0].message.content | |
| assert "Hello from Sport Scribe AI!" in response.choices[0].message.content |
🤖 Prompt for AI Agents
In ai-backend/tests/test_openai.py around lines 15 to 34, the
test_openai_connection function uses print statements instead of pytest
assertions, which is not appropriate for automated testing. Replace the print
statements with assertions to verify that the API key is set and that the
response from the OpenAI API contains the expected content. Use pytest's assert
statements to check these conditions and raise errors if they fail, removing all
print statements.
| try: | ||
| # Test with a simple completion | ||
| response = client.chat.completions.create( | ||
| model="gpt-4.1-nano", |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify the OpenAI model name is valid.
The model "gpt-4.1-nano" doesn't appear to be a valid OpenAI model name. Common models include "gpt-4", "gpt-4-turbo", "gpt-3.5-turbo".
🌐 Web query:
What are the current available OpenAI chat completion models as of 2024?
💡 Result:
As of mid-2025, the current available OpenAI chat completion models (via OpenAI API and platforms like Azure OpenAI) include several generations of GPT-4-based models, reasoning models, and specialized variants. The models differ in capability, context length, performance, and multimodal abilities. The principal models and their key features:
Main Available OpenAI Chat Completion Models (2024–2025):
| Model Name | Description / Capabilities | Availability / Comments |
|---|---|---|
| GPT-4o | Flagship multimodal model: supports text, vision, (in ChatGPT) audio | General Availability since May 2024[4][5][6] |
| GPT-4o mini | Small, cost-efficient version of GPT-4o; real-world performance advance | Introduced July 2024[8] |
| GPT-4.1 | Updated GPT-4 series; enhancements in reasoning and context size | Available in Azure OpenAI, previewed 2025[4] |
| GPT-4 Turbo (with Vision) | High-performance, large-context, vision-capable version | GA model as of April 2024[5] |
| GPT-4 (various versions) | Large context, advanced chat model (pre-Turbo/Turbo with Vision releases) | Evolving; older versions may be deprecated |
| GPT-3.5-turbo | Widely-used, cost-effective general-purpose chat model | Still available as a less powerful baseline |
| GPT-3.5-turbo-16k | GPT-3.5-turbo variant with extended (16k) context window | |
| o Reasoning Series* | “o1”, “o1-mini”, “o1-preview”, “o3”, “o3-pro”, “o4-mini” (specialized models for advanced reasoning, math, coding, etc.) | Preview and limited access; deployed for research and hard problem solving[6][8][5] |
| codex-mini | Code-focused model based on reasoning (o4) lineage; can also process text/images | Azure Foundry specialty[5] |
| gpt-4o-audio / gpt-4o-mini-audio | Audio/completions models for automatic speech tasks | Preview / specialized usage in Azure[4] |
Notes and Additional Context:
- GPT-4o is the default and most capable chat (and vision) model for most users, available in all OpenAI product tiers (Free, Plus, Team, Enterprise) and the API[6][1][5].
- GPT-4 Turbo with Vision and GPT-4.1 extend context windows and reasoning abilities, and offer improved performance, particularly in non-English and multimodal applications[4][5].
- Reasoning “o” models* (e.g., o1, o3, o4-mini) are released as previews. They target domains needing more extended reasoning, problem-solving, and critical thinking. API support and rate limits may vary, and some models lack certain features (e.g., tool use, memory) compared to GPT-4o[6][8][5].
- GPT-3.5-turbo and GPT-3.5-turbo-16k remain available as cost-effective options, but are no longer the cutting edge for new applications[3][5].
- Specialty and audio models (such as gpt-4o-mini-audio-preview) address non-text use cases like real-time audio completion or transcription in Azure[4][5].
Model Context and API Nuances:
- All the above models use the standardized Chat Completion message format (
role,contentobjects)—even if the back-end model architectures differ[1]. - Model access (and precise version) can vary by platform (OpenAI, Azure), API version, and organizational/region settings. Preview models may have additional limitations or be subject to deprecation without notice[5][4].
- For code generation and reasoning tasks, models such as codex-mini or the “o*” series may offer structured outputs, function calling, or extended tool-aware capabilities[5].
- Legacy “text-davinci-003” completion models are deprecated; for most “completion” style use-cases, you must now use “chat completion” style APIs and newer models, often with a suitable system prompt to mimic old completions[3][1].
Caveats:
- Preview and research models (e.g., GPT-4.5, o1-series) may have restricted access or limited documented support and are not recommended for critical production tasks until they achieve GA status[6][8][4].
- Model release cadence is high; names, limits, and availability change frequently. Always refer to the [OpenAI model documentation][7] or the Azure OpenAI model listing[5] for the latest, region-specific updates.
In summary, as of July 2025, the current official OpenAI chat completion models include GPT-4o, GPT-4 Turbo/Vision/4.1, various “o” series reasoning models, and selected legacy GPT-3.5-turbo options, along with specialized versions for audio and code[4][5][6][8][1][2].
Citations:
- 1: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/chatgpt
- 2: https://docs.litellm.ai/docs/providers/openai
- 3: https://community.openai.com/t/using-chat-models-as-completion-models/880393
- 4: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/whats-new
- 5: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models
- 6: https://help.openai.com/en/articles/7864572-what-is-the-chatgpt-model-selector
- 7: https://platform.openai.com/docs/models
- 8: https://help.openai.com/en/articles/9624314-model-release-notes
- 9: https://platform.openai.com/docs/models/o1
- 10: https://openai.com/index/introducing-o3-and-o4-mini/
Invalid OpenAI model name used
The value "gpt-4.1-nano" is not a recognized chat-completion model. Please replace it with one of the currently available names.
• File: ai-backend/tests/test_openai.py
Line 24
• Suggested valid models (pick one that matches your use case):
"gpt-4o""gpt-4o-mini""gpt-4.1"(Azure preview)"gpt-4-turbo"/"gpt-4-turbo-vision""gpt-3.5-turbo"/"gpt-3.5-turbo-16k"
Example change:
- model="gpt-4.1-nano",
+ model="gpt-4o-mini", # or another valid model from the list above📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| model="gpt-4.1-nano", | |
| model="gpt-4o-mini", # or another valid model from the list above |
🤖 Prompt for AI Agents
In ai-backend/tests/test_openai.py at line 24, the model name "gpt-4.1-nano" is
invalid. Replace it with a valid OpenAI model name such as "gpt-4o",
"gpt-4o-mini", "gpt-4.1", "gpt-4-turbo", "gpt-4-turbo-vision", "gpt-3.5-turbo",
or "gpt-3.5-turbo-16k" depending on your test requirements.
| logger.info( | ||
| f"Requesting fixtures with params: {payload}. Status: {response.status}" | ||
| ) |
There was a problem hiding this comment.
Potential injection vulnerability in log message.
The payload is logged directly without sanitization, but the payload should be sanitized before logging to prevent log injection attacks.
logger.info(
- f"Requesting fixtures with params: {payload}. Status: {response.status}"
+ f"Requesting fixtures with params: {sanitize_log_input(str(payload))}. Status: {response.status}"
)Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In ai-backend/tools/sports_apis.py around lines 129 to 131, the log message
includes the payload directly, which can lead to log injection vulnerabilities.
To fix this, sanitize or escape the payload data before including it in the log
message to ensure no malicious content can manipulate the logs. Use a safe
serialization method or explicitly escape special characters in the payload
before logging.
| if self.api_key: | ||
| self.headers = { | ||
| "x-rapidapi-key": self.api_key, | ||
| "x-rapidapi-host": "api-football-v1.p.rapidapi.com", | ||
| "Content-Type": "application/json", | ||
| } |
There was a problem hiding this comment.
Fix header key casing inconsistency with APIFootballClient.
This class uses lowercase header keys (x-rapidapi-key) while APIFootballClient uses uppercase (X-RapidAPI-Key). HTTP headers are case-insensitive, but consistency across the codebase is important.
if self.api_key:
self.headers = {
- "x-rapidapi-key": self.api_key,
- "x-rapidapi-host": "api-football-v1.p.rapidapi.com",
+ "X-RapidAPI-Key": self.api_key,
+ "X-RapidAPI-Host": "api-football-v1.p.rapidapi.com",
"Content-Type": "application/json",
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if self.api_key: | |
| self.headers = { | |
| "x-rapidapi-key": self.api_key, | |
| "x-rapidapi-host": "api-football-v1.p.rapidapi.com", | |
| "Content-Type": "application/json", | |
| } | |
| if self.api_key: | |
| self.headers = { | |
| "X-RapidAPI-Key": self.api_key, | |
| "X-RapidAPI-Host": "api-football-v1.p.rapidapi.com", | |
| "Content-Type": "application/json", | |
| } |
🤖 Prompt for AI Agents
In ai-backend/tools/test_api_auth.py around lines 26 to 31, update the header
keys from lowercase (e.g., "x-rapidapi-key") to match the uppercase style used
in APIFootballClient (e.g., "X-RapidAPI-Key") to maintain consistent header key
casing across the codebase.
| async def test_api_auth(self) -> list[dict[str, Any]]: | ||
|
|
||
| if not self.headers or "x-rapidapi-key" not in self.headers: | ||
| logger.error("Missing RapidAPI key in headers.") | ||
| return [] | ||
|
|
||
| payload = { | ||
| # "league": "39", | ||
| # "season": "2023", | ||
| "timezone": "Europe/London", | ||
| "date": "2023-02-23", | ||
| } | ||
|
|
||
| url = self.base_url + "/fixtures" | ||
|
|
||
| return await self._fetch_data(url, payload) | ||
|
|
There was a problem hiding this comment.
Fix return type inconsistency and update test date.
The method signature indicates it returns list[dict[str, Any]] but _fetch_data returns a stringified JSON string. Also, the hardcoded date "2023-02-23" is outdated.
async def test_api_auth(self) -> list[dict[str, Any]]:
if not self.headers or "x-rapidapi-key" not in self.headers:
logger.error("Missing RapidAPI key in headers.")
return []
payload = {
- # "league": "39",
- # "season": "2023",
"timezone": "Europe/London",
- "date": "2023-02-23",
+ "date": "2025-07-10", # Use a more recent date
}
# In _fetch_data method:
- data_stringified = json.dumps(data, indent=2)
- # logger.info(f"Response data: {data_stringified}")
- return data_stringified
+ return data.get("response", [])Also applies to: 73-91
🤖 Prompt for AI Agents
In ai-backend/tools/test_api_auth.py around lines 56 to 72 and 73 to 91, the
test_api_auth method's return type is declared as list[dict[str, Any]], but it
actually returns a JSON string from _fetch_data, causing a type mismatch. To fix
this, parse the JSON string returned by _fetch_data into a Python list of
dictionaries before returning it. Additionally, update the hardcoded date
"2023-02-23" to a more current date to keep the test relevant.
| #!/bin/bash | ||
| # Comprehensive code quality fix script |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to the shell script.
The script should exit on errors and handle missing tools gracefully.
#!/bin/bash
+set -e # Exit on error
# Comprehensive code quality fix script📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| #!/bin/bash | |
| # Comprehensive code quality fix script | |
| #!/bin/bash | |
| set -e # Exit on error | |
| # Comprehensive code quality fix script |
🤖 Prompt for AI Agents
In scripts/lint-fix.sh at lines 1 to 2, the script lacks error handling and does
not check for required tools. Add 'set -e' at the beginning to make the script
exit immediately on any error. Also, include checks for the presence of
necessary tools before running commands, and provide user-friendly error
messages if tools are missing to handle such cases gracefully.
| echo "📝 Formatting with Black..." | ||
| black . | ||
|
|
||
| # Fix AI Backend linting | ||
| fix_ai_backend() { | ||
| echo "🧠 Fixing AI Backend..." | ||
| cd ai-backend | ||
| echo "📦 Sorting imports with isort..." | ||
| isort . | ||
|
|
||
| # Activate virtual environment | ||
| source venv/bin/activate 2>/dev/null || echo "Using system Python" | ||
| echo "🔍 Linting and fixing with Ruff..." | ||
| ruff check . --fix | ||
|
|
||
| # Auto-fix with ruff | ||
| ruff check --fix . | ||
| echo "📋 Formatting with Ruff..." | ||
| ruff format . |
There was a problem hiding this comment.
Review the order of formatters to prevent conflicts.
Running isort after black might undo some of Black's formatting decisions. Black is designed to be the final formatter in the chain. Additionally, both black and ruff format are formatters which might conflict.
Consider this order to prevent conflicts:
-echo "📝 Formatting with Black..."
-black .
-
echo "📦 Sorting imports with isort..."
isort .
echo "🔍 Linting and fixing with Ruff..."
ruff check . --fix
-echo "📋 Formatting with Ruff..."
-ruff format .
+echo "📝 Formatting with Black..."
+black .Alternatively, use either Black OR Ruff format, not both, as they may have conflicting formatting rules.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| echo "📝 Formatting with Black..." | |
| black . | |
| # Fix AI Backend linting | |
| fix_ai_backend() { | |
| echo "🧠 Fixing AI Backend..." | |
| cd ai-backend | |
| echo "📦 Sorting imports with isort..." | |
| isort . | |
| # Activate virtual environment | |
| source venv/bin/activate 2>/dev/null || echo "Using system Python" | |
| echo "🔍 Linting and fixing with Ruff..." | |
| ruff check . --fix | |
| # Auto-fix with ruff | |
| ruff check --fix . | |
| echo "📋 Formatting with Ruff..." | |
| ruff format . | |
| echo "📦 Sorting imports with isort..." | |
| isort . | |
| echo "🔍 Linting and fixing with Ruff..." | |
| ruff check . --fix | |
| echo "📝 Formatting with Black..." | |
| black . |
🤖 Prompt for AI Agents
In scripts/lint-fix.sh between lines 6 and 16, the current order runs black
before isort and then both black and ruff format, which can cause formatting
conflicts. To fix this, reorder the commands to run isort first, then black as
the final formatter, and choose either black or ruff format but not both to
avoid conflicts. Adjust the script so that isort sorts imports first, black
formats the code last, and remove one of the two formatters (black or ruff
format) to maintain consistent formatting.
1 participant
Description
Set Up API-Football Client Authentication
Type of Change
Changes Made
Testing
How has this been tested?
Test Configuration:
Platform Impact
Which parts of the system are affected?
Breaking Changes
Does this PR introduce any breaking changes?
If yes, describe the breaking changes and migration path:
Checklist
Before requesting a review, please ensure:
Screenshots (if applicable)
Add screenshots to help explain your changes.
Related Issues
Closes #(issue_number)
Related to #(issue_number)
Additional Notes
Any additional information that reviewers should know.
Summary by CodeRabbit
New Features
Bug Fixes
Refactor
Style
Chores