feat: Add tracing

[monoxgas]

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PR Summary

Overview of Changes

This PR introduces several significant modifications to the Rigging LLM framework, aiming to simplify the usage of language models in production code. It enhances the framework's compatibility and utility by refining the project's README, expanding documentation, and integrating tracing support with the Logfire library for better activity tracking. Additionally, there have been updates to internal logic and functionality, particularly in handling chat and completion pipelines, making the codebase more robust and flexible.

Key Modifications

1. Revision of README and Documentation: Phrasing in the README and documentation pages has been streamlined for clarity. This includes simplifying descriptions and correcting typographical errors to improve readability and comprehension.
2. Integration of Tracing Support: A notable feature is the integration of tracing support with Logfire. This addition allows users to track behavior and activity more efficiently, which is crucial for debugging and monitoring purposes.
3. Enhancements in ChatPipeline and CompletionPipeline Logic: The PR includes modifications that improve the error handling and callback mechanisms within the ChatPipeline and CompletionPipeline classes. These changes ensure that the framework is more resilient and better suited for complex applications.
4. Addition of Tracing Documentation: New documentation for tracing functionality has been added, providing users with guidelines on leveraging the integrated tracing capabilities to monitor execution and performance closely.
5. Code Refinement and Error Handling Improvements: The PR makes several adjustments to internal logic, particularly in error messaging and the structuring of callback functionalities, aiming to enhance the stability and developer experience.

Potential Impact

• Enhanced Monitoring and Debugging: With the integration of Logfire for tracing, developers can now have a more in-depth overview of their applications' performance and issues, leading to quicker troubleshooting and optimization.
• Improved Developer Experience: The simplifications and clarifications in the README and documentation make it easier for new users to understand and make use of the Rigging LLM framework effectively.
• Increased Framework Robustness: The improvements and bug fixes in key components such as chat and completion pipelines contribute to a more stable and reliable framework, potentially reducing runtime errors and unexpected behaviors.
• Broader Utility and Compatibility: By addressing feedback and making iterative enhancements, the framework broadens its appeal to a wider range of use cases and development scenarios, increasing its utility and compatibility in production environments.

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