style(agent_utils.py): format imports and code for better readability
refactor(agent_utils.py): standardize model name and cost calculation logic for clarity and maintainability
chore(anthropic_callback_handler.py): create a new file for the AnthropicCallbackHandler implementation and related functions
* chore: refactor code for improved readability and maintainability
- Standardize variable naming conventions for consistency.
- Improve logging messages for better clarity and debugging.
- Remove unnecessary imports and clean up code structure.
- Enhance error handling and logging in various modules.
- Update comments and docstrings for better understanding.
- Optimize imports and organize them logically.
- Ensure consistent formatting across files for better readability.
- Refactor functions to reduce complexity and improve performance.
- Add missing type hints and annotations for better code clarity.
- Improve test coverage and organization in test files.
style(tests): apply consistent formatting and spacing in test files for improved readability and maintainability
* chore(tests): remove redundant test for ensure_tables_created with no models to streamline test suite and reduce maintenance overhead
* fix(memory.py): update is_binary_file function to correctly identify binary files by returning True for non-text mime types
style(tests): format code for better readability and consistency in test files
test(tests): update assertions and test cases for better clarity and maintainability
feat(ciayn_agent.py): pass config to CiaynAgent for improved functionality
fix(ciayn_agent.py): handle tool execution errors more gracefully with msg_list
feat(fallback_handler.py): enhance handle_failure method to utilize msg_list for better context
feat(fallback_handler.py): implement init_msg_list to manage message history effectively
test(test_fallback_handler.py): add unit tests for init_msg_list to ensure correct behavior
fix(fallback_handler.py): update tool invocation logic to handle missing tools and raise appropriate exceptions for better error handling
test(tests): add comprehensive tests for fallback handler functionality, including loading models, extracting tool names, and invoking tools to ensure robustness and reliability
refactor(fallback_handler.py): improve error handling by raising a specific exception when all fallback models fail
test(fallback_handler.py): update tests to reflect changes in the fallback handler's error handling and initialization
fix(test_llm.py): update error messages in tests for unsupported providers to be more descriptive and accurate
fix(agent_utils.py): set default value of fallback_handler to None in run_agent_with_retry
chore(ciayn_agent.py): comment out debug log for generated code to reduce verbosity
fix(fallback_handler.py): change fallback_enabled config key to experimental_fallback_handler for better clarity
refactor(test_ciayn_agent.py): update invoke method in DummyModel to return AIMessage instead of a custom Response class
test(test_ciayn_agent.py): comment out test_retry_logic_with_failure_recovery for future implementation and focus on existing tests
feat(main.py): add --experimental-fallback-handler argument to enable fallback handler
fix(agent_utils.py): modify init_fallback_handler to check for experimental fallback handler flag
fix(config.py): increase DEFAULT_MAX_TOOL_FAILURES to allow more retries before failure
fix(ciayn_agent.py): reset fallback handler after executing tool to ensure proper state management
style(fallback_handler.py): format method signature for better readability
refactor(agent_utils.py): extract fallback handling logic from run_agent_with_retry to improve code readability
fix(ciayn_agent.py): update stream method parameter name for consistency
chore(agents_alias.py): reorder import statements to follow best practices
style(fallback_handler.py): reorder exception imports for consistency and clarity
AI Christianson
Ariel Frischer