docs: Update installation guide to use uv tool package manager

[tonykipkemboi]

Changes made:

• Updated installation guide to use UV as the primary package manager
• Added detailed UV installation instructions for different platforms
• Simplified the installation process with clear step-by-step instructions
• Improved quickstart guide formatting and clarity
• Added navigation step in quickstart guide
• Updated project structure to include knowledge directory
• Removed outdated Poetry-related content
• Streamlined the documentation flow for better readability

Closes #2126

[joaomdmoura]

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

Code Review Comments for PR - Documentation Updates

Overview

The recent changes involved thorough updates to three main documentation files which significantly improve user experience and clarify the installation process and tool usage for the CrewAI platform.

Findings and Recommendations

1. Qdrant Vector Search Tool Documentation

• Strengths:

  • Well-structured documentation detailing installation steps with clear examples.
  • Covers configuration options and error handling effectively.

• Improvements:

  • Version Requirements:

    • Missing version requirements should be explicitly stated to prevent compatibility issues.

      version_requirements:
        crewai: ">=0.12.0"
        qdrant-client: ">=1.6.0"

  • Error Handling Example:

    • Include specific error handling examples for better clarity.

      try:
          results = qdrant_tool.search("query")
      except QdrantConnectionError:
          logger.error("Failed to connect to Qdrant server")
          raise

  • Add Type Hints:

    • Adding type hints in function definitions will improve code clarity and usability for newcomers.

      from typing import List
      
      def custom_embeddings(text: str) -> List[float]:
          # Implementation
          pass

2. Installation Guide Updates

• Strengths:

  • Great clarity on migration to the UV package manager and well-structured installation steps.

• Improvements:

  • Version Compatibility Table:

    • A compatibility table for different versions of CrewAI and Python would enhance user understanding.

      ## Version Compatibility
      
      | CrewAI Version | Python Version | UV Version |
      |----------------|----------------|------------|
      | >=0.12.0       | >=3.10        | >=0.1.0    |

  • Troubleshooting Section:

    • Adding a section for troubleshooting common installation issues can significantly help users.

3. Quickstart Guide Updates

• Strengths:

  • Simplified installation process and clear sequencing of steps enhances the user onboarding experience.

• Improvements:

  • Input Validation Example:

    • An example for input validation will ensure users maintain data integrity.

      from pydantic import BaseModel, Field
      
      class CrewInput(BaseModel):
          topic: str = Field(..., min_length=3, description="Research topic")
          depth: int = Field(default=3, ge=1, le=5, description="Research depth")

  • Environment Template:

    • Including an example of how to set up the required credentials in a .env file is crucial for securing sensitive information.

      # Example .env file
      OPENAI_API_KEY=sk-...
      

General Recommendations

• Implement Linting and Type Checking:
  • Introducing code linting configuration for consistent coding standards will improve the overall code quality.
  • Include type checkers in the development setup to ensure type safety and catch potential errors.

Conclusion

The updates across the documentation files significantly boost the clarity of the installation process and tool usage. Suggested improvements aim to enhance robustness and user experience, making CrewAI more accessible.

Overall, the initiative taken in these updates is commendable, providing a solid foundation for users engaging with the CrewAI platform. Integrating these suggestions will further enhance the documentation's utility and comprehensiveness.

This comment highlights the strengths and areas for improvement based on analyzed reports while relating it back to the established documentation structure and user needs. The connected suggestions also form a roadmap for continuous enhancements in line with user experiences.