* feat: add training logic to agent and crew
* feat: add training logic to agent executor
* feat: add input parameter to cli command
* feat: add utilities for the training logic
* feat: polish code, logic and add private variables
* feat: add docstring and type hinting to executor
* feat: add constant file, add constant to code
* feat: fix name of training handler function
* feat: remove unused var
* feat: change file handler file name
* feat: Add training handler file, class and change on the code
* feat: fix name error from file
* fix: change import to adapt to logic
* feat: add training handler test
* feat: add tests for file and training_handler
* feat: add test for task evaluator function
* feat: change text to fit in-screen
* feat: add test for train function
* feat: add test for agent training_handler function
* feat: add test for agent._use_trained_data
* fix: 'from datetime import datetime for logging' to print the timestamp
* fix: correct default model (gpt-4o), correct token counts, and correct TaskOutput attributes (added agent)
* test: verify Task callback data is an instance of TaskOutput
* fix: fix test actually running
* fix: fix test to not send request to openai
* fix: fix linting to remove cli files
* fix: exclude only files that breaks black
* fix: Fix all Ruff checkings on the code and Fix Test with repeated name
* fix: Change linter name on yml file
* feat: update pre-commit
* feat: remove need for isort on the code
* feat: remove black linter
* feat: update tests yml to try to fix the tests gh action
* feature: human input per task
* Update executor.py
* Update executor.py
* Update executor.py
* Update executor.py
* Update executor.py
* feat: change human input for unit testing
added documentation and unit test
* Create test_agent_human_input.yaml
add yaml for test
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Co-authored-by: João Moura <joaomdmoura@gmail.com>
This now will allow to add a max_inter option to agents while also making sure to force the agent to give it's best final answer before running out of it's max_inter.
* Refactoring task cache to be a tool
The previous implementation of the task caching system was early exiting
the agent executor due to the fact it was returning an AgentFinish object.
This now refactors it to use a cache specific tool that is dynamically
added and forced into the agent in case of a task execution that was
already executed with the same input.
* Adding tool caching a loop execution prevention.
This adds some guardrails, to both prevent the same tool to be used
consecutively and also caching tool's results across the entire crew
so it cuts down execution time and eventual LLM calls.
This plays a huge role for smaller opensource models that usually fall
into those behaviors patterns.
It also includes some smaller improvements around the tool prompt and
agent tools, all with the same intention of guiding models into
better conform with agent instructions.
Update to Pydantic v2:
Transitioned all references from pydantic.v1 to pydantic (v2), ensuring compatibility with the latest Pydantic features and improvements.
Affected components include agent tools, prompts, crew, and task modules.
Refactoring & Alignment with Pydantic Standards:
Refactored the agent module away from traditional __init__ to align more closely with Pydantic best practices.
Updated the crew module to Pydantic v2 and enhanced configurations, allowing JSON and dictionary inputs. Additionally, some (not all) exceptions have been migrated to leverage Pydantic's error-handling capabilities.
Enhancements to Validators and Typings:
Improved validators and type annotations across multiple modules, enhancing code readability and maintainability.
Streamlined the validation process in line with Pydantic v2's methodologies.
Import and Configuration Adjustments:
Updated to test-related absolute imports due to issues with Pytest finding packages through relative imports.
