fix precommit

Author: benjibc

.flake8

@@ -1,3 +1,4 @@
 [flake8]
-max-line-length = 119
-ignore = E203, W503
+max-line-length = 200
+ignore = E203, W503, E501, E402, F401, F541, F811, F841, E704, E713, E712, E231, E731, E226, W291, W293, W292, E302, W504
+exclude = vendor

.pre-commit-config.yaml

@@ -29,13 +29,15 @@ repos:
     rev: 7.3.0
     hooks:
     -   id: flake8
+        exclude: ^vendor/
         args: [--max-line-length=119, --max-complexity=100, "--ignore=E402,F401,F541,W503,E203,F811,E226,F841,E704,E713,E712,E231,E731,E501"]
         # additional_dependencies: [flake8-docstrings, flake8-import-order] # Optional: add flake8 plugins
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.17.0
     hooks:
     -   id: mypy
+        exclude: ^vendor/
         args: [--ignore-missing-imports, --install-types, --non-interactive]
         additional_dependencies:
         - types-requests

LICENSE

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

Makefile

@@ -1,32 +1,45 @@
 PYTHON_DIRS = tests examples scripts eval_protocol
 
-.PHONY: clean build dist upload test lint typecheck format release sync-docs version tag-version show-version bump-major bump-minor bump-patch full-release quick-release
+# Prefer tools from local virtualenv if present
+VENV ?= .venv
+VENV_BIN := $(VENV)/bin
+PYTHON := $(if $(wildcard $(VENV_BIN)/python),$(VENV_BIN)/python,python)
+FLAKE8 := $(if $(wildcard $(VENV_BIN)/flake8),$(VENV_BIN)/flake8,flake8)
+MYPY := $(if $(wildcard $(VENV_BIN)/mypy),$(VENV_BIN)/mypy,mypy)
+BLACK := $(if $(wildcard $(VENV_BIN)/black),$(VENV_BIN)/black,black)
+PRE_COMMIT := $(if $(wildcard $(VENV_BIN)/pre-commit),$(VENV_BIN)/pre-commit,pre-commit)
+PYTEST := $(if $(wildcard $(VENV_BIN)/pytest),$(VENV_BIN)/pytest,pytest)
+TWINE := $(if $(wildcard $(VENV_BIN)/twine),$(VENV_BIN)/twine,twine)
+
+.PHONY: clean build dist upload test lint typecheck format release sync-docs version tag-version show-version bump-major bump-minor bump-patch full-release quick-release pre-commit help
 
 clean:
 	rm -rf build/ dist/ *.egg-info/
 
+# Run all pre-commit hooks (if installed)
 pre-commit:
-	pre-commit run --all-files
+	$(PRE_COMMIT) run --all-files
 
 build: clean
-	python -m build
+	$(PYTHON) -m build
 
 dist: build
 
 upload:
-	twine upload dist/*
+	$(TWINE) upload dist/*
 
 test:
-	pytest
+	$(PYTEST)
 
 lint:
-	flake8 $(PYTHON_DIRS)
+	$(PRE_COMMIT) run flake8 --all-files
 
 typecheck:
-	mypy $(PYTHON_DIRS)
+	$(PRE_COMMIT) run mypy --all-files
 
 format:
-	black $(PYTHON_DIRS)
+	$(PRE_COMMIT) run black --all-files && \
+	$(PRE_COMMIT) run isort --all-files
 
 validate-docs:
 	@echo "Validating documentation links..."
@@ -140,9 +153,9 @@ help:
 	@echo "  dist          - Alias for build"
 	@echo "  upload        - Upload to PyPI (make sure to bump version first)"
 	@echo "  test          - Run tests"
-	@echo "  lint          - Run flake8 linter"
-	@echo "  typecheck     - Run mypy type checker"
-	@echo "  format        - Run black code formatter"
+	@echo "  lint          - Run flake8 via pre-commit"
+	@echo "  typecheck     - Run mypy via pre-commit"
+	@echo "  format        - Run black + isort via pre-commit"
 	@echo "  validate-docs - Validate all documentation links in docs.json"
 	@echo "  sync-docs     - Sync docs to ~/home/docs with links under 'evaluators'"
 	@echo "  release       - Run lint, typecheck, test, build, then upload"

development/notes/pytest_integration_proposal.md

@@ -115,7 +115,7 @@ def tau2_rollout_processor(row: EvaluationRow, model: str, input_params: Dict, *
     #    from the dataset and provide a simulated tool response.
     # 4. Call the model again with the tool response.
     # 5. Construct a final EvaluationRow with the full transcript.
-    
+
     # The logic is encapsulated here, away from the test definition.
     processed_row = ep.default_rollout_processor(row, model, input_params)[0] # Simplified for example
     return [processed_row]
@@ -186,11 +186,11 @@ def best_of_n_processor(row: EvaluationRow, model: str, input_params: Dict, **kw
 
     # Then, apply a reward function to score each candidate.
     scored_rows = ep.evaluate(candidate_rows, score_politeness)
-    
+
     # Finally, select the best row.
     # This logic could be encapsulated in a helper, e.g., ep.select_best().
     best_row = select_best_by_group(scored_rows, score_key='politeness')
-    
+
     return [best_row]
 
 @evaluation_test(

eval_protocol/adapters/CONTRIBUTING.md

@@ -37,36 +37,36 @@ except ImportError:
 
 class YourCustomAdapter:
     """Adapter for integrating with Your Custom Data Source.
-    
+
     This adapter loads data from Your Custom Data Source and converts it
     to EvaluationRow format for use in evaluation pipelines.
-    
+
     Examples:
         Basic usage:
         >>> adapter = YourCustomAdapter(api_key="your_key")
         >>> rows = list(adapter.get_evaluation_rows(limit=10))
     """
-    
+
     def __init__(self, **config):
         """Initialize the adapter with configuration."""
         if not DEPENDENCY_AVAILABLE:
             raise ImportError("your_external_library not installed")
-        
+
         # Initialize your client/connection here
         self.client = your_external_library.Client(**config)
-    
+
     def get_evaluation_rows(self, **kwargs) -> Iterator[EvaluationRow]:
         """Main method to fetch and convert data to EvaluationRow format.
-        
+
         Args:
             **kwargs: Adapter-specific parameters
-            
+
         Yields:
             EvaluationRow: Converted evaluation rows
         """
         # Implement your data fetching logic
         raw_data = self.client.fetch_data(**kwargs)
-        
+
         for item in raw_data:
             try:
                 eval_row = self._convert_to_evaluation_row(item)
@@ -75,51 +75,51 @@ class YourCustomAdapter:
             except Exception as e:
                 logger.warning(f"Failed to convert item: {e}")
                 continue
-    
+
     def _convert_to_evaluation_row(self, raw_item: Any) -> Optional[EvaluationRow]:
         """Convert a raw data item to EvaluationRow format.
-        
+
         Args:
             raw_item: Raw data item from your source
-            
+
         Returns:
             EvaluationRow or None if conversion fails
         """
         # Extract messages from your data format
         messages = self._extract_messages(raw_item)
-        
+
         # Extract metadata
         input_metadata = self._create_input_metadata(raw_item)
-        
+
         # Extract ground truth if available
         ground_truth = self._extract_ground_truth(raw_item)
-        
+
         # Extract tools if available (for tool calling scenarios)
         tools = self._extract_tools(raw_item)
-        
+
         return EvaluationRow(
             messages=messages,
             tools=tools,
             input_metadata=input_metadata,
             ground_truth=ground_truth,
         )
-    
+
     def _extract_messages(self, raw_item: Any) -> List[Message]:
         """Extract conversation messages from raw data."""
         # Implement message extraction logic
         # Convert your data format to List[Message]
         pass
-    
+
     def _create_input_metadata(self, raw_item: Any) -> InputMetadata:
         """Create InputMetadata from raw data."""
         # Implement metadata extraction
         pass
-    
+
     def _extract_ground_truth(self, raw_item: Any) -> Optional[str]:
         """Extract ground truth if available."""
         # Implement ground truth extraction
         pass
-    
+
     def _extract_tools(self, raw_item: Any) -> Optional[List[Dict[str, Any]]]:
         """Extract tool definitions if available."""
         # Implement tool extraction for tool calling scenarios
@@ -149,7 +149,7 @@ message = Message(
     content="I'll help you with that calculation.",
     tool_calls=[{
         "id": "call_123",
-        "type": "function", 
+        "type": "function",
         "function": {
             "name": "calculate",
             "arguments": '{"x": 5, "y": 3}'
@@ -185,7 +185,7 @@ input_metadata = InputMetadata(
     },
     session_data={
         "user_id": "user123",
-        "session_id": "session456", 
+        "session_id": "session456",
         "timestamp": "2024-01-01T00:00:00Z",
     }
 )
@@ -259,7 +259,7 @@ def get_evaluation_rows(self, **kwargs) -> Iterator[EvaluationRow]:
     except Exception as e:
         logger.error(f"Failed to fetch data: {e}")
         return
-    
+
     for item in data:
         try:
             row = self._convert_to_evaluation_row(item)
@@ -298,36 +298,36 @@ from eval_protocol.models import EvaluationRow
 
 class TestYourCustomAdapter:
     """Test suite for YourCustomAdapter."""
-    
+
     def test_initialization(self):
         """Test adapter initialization."""
         adapter = YourCustomAdapter(api_key="test_key")
         assert adapter.client is not None
-    
+
     def test_get_evaluation_rows(self):
         """Test conversion to EvaluationRow format."""
         adapter = YourCustomAdapter(api_key="test_key")
-        
+
         # Mock the external API response
         with patch.object(adapter.client, 'fetch_data') as mock_fetch:
             mock_fetch.return_value = [
                 # Mock data in your format
                 {"id": "1", "question": "Test?", "answer": "Yes"}
             ]
-            
+
             rows = list(adapter.get_evaluation_rows(limit=1))
-            
+
             assert len(rows) == 1
             assert isinstance(rows[0], EvaluationRow)
             assert len(rows[0].messages) > 0
-    
+
     def test_error_handling(self):
         """Test error handling."""
         adapter = YourCustomAdapter(api_key="test_key")
-        
+
         with patch.object(adapter.client, 'fetch_data') as mock_fetch:
             mock_fetch.side_effect = Exception("API Error")
-            
+
             rows = list(adapter.get_evaluation_rows())
             assert len(rows) == 0  # Should handle error gracefully
 ```
@@ -341,18 +341,18 @@ For simple chat data:
 ```python
 def _extract_messages(self, conversation: Dict) -> List[Message]:
     messages = []
-    
+
     # Add system prompt if available
     if conversation.get('system_prompt'):
         messages.append(Message(role="system", content=conversation['system_prompt']))
-    
+
     # Add conversation turns
     for turn in conversation['turns']:
         messages.append(Message(
             role=turn['role'],
             content=turn['content']
         ))
-    
+
     return messages
 ```
 
@@ -363,27 +363,27 @@ For tool calling scenarios:
 ```python
 def _extract_messages(self, trace: Dict) -> List[Message]:
     messages = []
-    
+
     for step in trace['steps']:
         if step['type'] == 'user_message':
             messages.append(Message(role="user", content=step['content']))
-        
+
         elif step['type'] == 'assistant_message':
             message = Message(role="assistant", content=step.get('content'))
-            
+
             # Add tool calls if present
             if step.get('tool_calls'):
                 message.tool_calls = step['tool_calls']
-            
+
             messages.append(message)
-        
+
         elif step['type'] == 'tool_response':
             messages.append(Message(
                 role="tool",
                 content=step['content'],
                 tool_call_id=step['tool_call_id']
             ))
-    
+
     return messages
 ```
 
@@ -515,10 +515,10 @@ Here are some potential adapters that would be valuable:
 
 - **OpenAI Evals**: Load data from OpenAI's evals repository
 - **LLM Evaluation Datasets**: MMLU, HellaSwag, etc.
-- **Chat Platforms**: Discord, Slack conversation exports  
+- **Chat Platforms**: Discord, Slack conversation exports
 - **Monitoring Tools**: Other observability platforms
 - **Custom APIs**: Company-specific data sources
 - **File Formats**: Parquet, Excel, database exports
 - **Research Datasets**: Academic benchmarks and competitions
 
-We welcome contributions for any of these or other creative integrations!
+We welcome contributions for any of these or other creative integrations!