* 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.
