Docs: Added expected_output field to examples in multimodal agents how-to guide

[juanfiguera]

Added expected_output field to tasks to prevent ValidationError from Pydantic.

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #1971

Overview

This pull request introduces the expected_output field to task definitions in the multimodal agents documentation, aiming to prevent ValidationError issues from Pydantic, which is crucial for ensuring data validation in Python applications.

Positive Changes

• The inclusion of the expected_output field significantly enhances the clarity and correctness of task definitions.
• This change effectively addresses potential validation errors encountered by users, ensuring consistent output expectations for tasks.
• The formatting and clarity of the added fields maintain consistency with existing documentation, improving the coherence of guidelines provided.

Suggested Improvements

While the updates are commendable, I recommend these enhancements to further improve the documentation:

1. Add Type Hints Documentation:
   Including clear type hints for expected_output would guide users in understanding what is expected of this field.

   # Create a task for image analysis
   task = Task(
       description="Analyze the product image at https://example.com/product.jpg and provide a detailed description",
       expected_output="A detailed description of the product image",  # Type: str
       agent=image_analyst
   )

2. Include Validation Rules Documentation:
   A note explaining Pydantic's validation requirements would provide clarity on how to format the expected_output.

   ---
   note: The `expected_output` field is required by Pydantic for task validation. It should describe the expected format and content of the task's output.
   ---

3. Enhance Example Clarity:
   Improving the expected output formatting, particularly for more complex tasks, would aid user comprehension.

   inspection_task = Task(
       description="""
       Analyze the following aspects of the manufacturing process:
       1. Quality control measures
       2. Safety protocols
       3. Compliance with standards
       Provide a detailed report highlighting any issues found.
       """,
       expected_output="""
       A detailed report containing:
       - Quality control assessment
       - Safety protocol evaluation
       - Compliance status
       - Identified issues and recommendations
       """,
       agent=expert_analyst
   )

4. Error Handling Documentation:
   Adding a section on error handling related to output validation would preemptively inform users of common pitfalls and how to address them.

   ### Error Handling
   If the task execution doesn't match the expected_output format, a ValidationError will be raised. Example error handling:
   ```python
   try:
       result = task.execute()
   except ValidationError as e:
       print(f"Output validation failed: {e}")

Overall Assessment

The changes are essential for proper Pydantic validation and are well-implemented. The documentation updates are clear and necessary for enhancing user experience. However, including additional contextual guidance and structured examples would significantly bolster the documentation's utility and user-friendliness.

Priority Recommendations

1. Integrate type hints into documentation clearly defining expected argument types.
2. Include validation rules concerning Pydantic requirements to assist user understanding.
3. Improve the clarity and structure of examples, particularly in defining expected outputs.
4. Provide clear guidance on error handling related to task execution and validation failures.

In summary, while the PR introduces critical enhancements for improving the robustness and usability of the multimodal agents documentation, implementing these suggested improvements will foster greater clarity and assist users in better meeting validation requirements while defining tasks.