docs: Make "Changing the LLM" model-agnostic

[markmcd]

For the string ID tab, replaced the combined OAI+Anthro example with 4 tabs: a default agnostic view and 3 concrete examples (OpenAI, Google and Anthropic).

And for the LLM Class tab, updated LLM() code to be agnostic to match other guides.

[joaomdmoura]

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

Code Review Comment for PR #2914 - LLM Documentation Updates

Overview

The pull request enhances the llm-connections.mdx documentation with improved organization, making the examples more model-agnostic and clarifying the differences for various providers, including OpenAI, Google, and Anthropic.

Positive Aspects

1. Organized Documentation: The introduction of separate tabs for different providers significantly boosts usability and clarity.
2. Model-Agnostic Examples: By providing examples that do not tie directly to one provider, the documentation reflects flexibility in system usage.
3. Improved Cross-Referencing: Added links to relevant sections enhance the overall navigability of the documentation.

Identified Issues and Suggestions

1. Inconsistent Model IDs

• Issue: Model IDs vary between examples (e.g., gpt-4o-mini vs. gpt-4).
• Suggestion: Standardize the use of model identifiers across all examples for clarity.

  # Default model reference
  OPENAI_MODEL_NAME = "gpt-4"  # Ensure consistency

2. Missing Version Information

• Issue: Code examples lack indications of minimum version requirements.
• Suggestion: Introduce a version information header at the top of the documentation.

  ---
  title: LLM Connections
  description: Configure different LLM providers with CrewAI
  minVersion: 1.x.x  # Define the minimum version required
  ---

3. Incomplete Error Handling Documentation

• Issue: Examples do not cover potential API errors.
• Suggestion: Incorporate an error handling section within the documentation.

  try:
      llm = LLM(
          model="model-id-here",
          api_key="your-api-key-here",
          base_url="url-if-needed",
          temperature=0.7,
      )
  except Exception as e:
      logger.error(f"Failed to initialize LLM: {str(e)}")
      raise

4. Environment Variable Documentation

• Issue: Documentation about environment variable configuration is insufficient.
• Suggestion: Expand this section to include complete instructions.

  ## Environment Variables
  Configure your LLM using environment variables:
  
  ```bash
  export OPENAI_API_KEY="your-key"
  export OPENAI_MODEL_NAME="gpt-4"

5. Code Style Improvements

• Issue: The code formatting and comments lack consistency.
• Suggestion: Ensure uniform formatting and commenting style throughout.

  from crewai import Agent, LLM
  
  DEFAULT_TEMPERATURE = 0.7
  DEFAULT_MODEL = "model-id-here"
  
  llm = LLM(
      model=DEFAULT_MODEL,
      api_key="your-api-key-here",
      base_url="url-if-needed",
      temperature=DEFAULT_TEMPERATURE,
  )

Security Considerations

1. Highlight warnings about API key security best practices.
2. Recommend using environment variables for API keys instead of hardcoding them.
3. Address base URL validation for custom endpoints to prevent misuse.

Documentation Structure Improvements

1. Incorporate a table of contents to aid in navigation.
2. Include a troubleshooting section for common issues and resolutions.
3. Provide examples of typical error messages to guide users.

Final Thoughts

The changes in this pull request mark a significant improvement in the documentation for configuring LLMs within the CrewAI framework. However, further enhancements are crucial in areas such as error handling, versioning, and user guidance to ensure a comprehensive user experience. Emphasizing standardization and providing clear instructions will enhance the documentation's usability, catering both to novice and experienced users.

By addressing the outlined suggestions, the documentation will become more robust, user-friendly, and maintainable.

[tonykipkemboi]

Thank you!