Use gpt-4-turbo-preview model

[pascal-zarrad]

Support gpt-4-turbo-preview as heavy model. This is cheaper than gpt-4 and provides a huge context.

Summary by CodeRabbit

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• New Feature: Added support for the gpt-4-turbo-preview model as a heavy model, replacing gpt-4. This allows users to utilize the latest AI model for their tasks.
• Refactor: Improved code readability and maintainability by replacing the if-else chain in TokenLimits constructor with a switch-case.
• Chore: Updated file exclusions and default values for model selection to align with the new changes.

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Summary by CodeRabbit

• New Features
  • Updated token limits for enhanced performance, including support for the 'gpt-4-turbo-preview' model with increased limits and an updated knowledge cutoff date.

[coderabbitaidev]

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Walkthrough

The recent update enhances the TokenLimits class by incorporating a switch statement for setting token limits based on the model type. A notable addition is the case for 'gpt-4-turbo-preview', which introduces higher token limits and a unique knowledgeCutOff date, reflecting an optimization for newer model capabilities.

Changes

File	Change Summary
src/.../limits.ts	Updated TokenLimits with a switch statement; added 'gpt-4-turbo-preview' case for increased token limits and a new knowledgeCutOff date.

🐇✨
In the realm of code, where logic plays,
A rabbit hopped, through the syntax maze.
With a switch and a case, it leaped ahead,
Setting limits high, where none dare tread.
'GPT-4 Turbo', it whispered with glee,
A new frontier, for you and for me.
🌟📚

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[github-actions]

CodeRabbit

Walkthrough

This update enhances the GitHub PR template and TokenLimits constructor to support the gpt-4-turbo-preview model as a heavy model, replacing gpt-4. It also improves file exclusions and default values for model selection. The code readability and maintainability have been improved by replacing the if-else chain in TokenLimits constructor with a switch-case.

Changes

File(s)	Summary
action.yml, src/limits.ts	The GitHub PR template and TokenLimits constructor now support gpt-4-turbo-preview as a heavy model, replacing gpt-4. Default values for model selection and file exclusions have been updated. The if-else chain in TokenLimits constructor has been replaced with a switch-case for better readability and maintainability.

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[github-actions]

CodeRabbit

Commits

Files that changed from the base of the PR and between d5ec397 and bddbc9f commits.

Files selected (2)

• action.yml (4)
• src/limits.ts (1)

Files ignored due to filter (1)

• dist/index.js

Review comments generated (6)

• Review: 4
• LGTM: 2

---

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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between d5ec397 and bddbc9f.

Files ignored due to path filters (2)

• action.yml is excluded by: !**/*.yml
• dist/index.js is excluded by: !dist/**

Files selected for processing (1)

• src/limits.ts (1 hunks)

Additional comments: 2

src/limits.ts (2)

• 22-26: The addition of the gpt-4-turbo-preview case with increased token limits and a distinct knowledgeCutOff date is aligned with the PR objectives to support the gpt-4-turbo-preview model. This change effectively utilizes the model's capabilities for handling larger contexts. However, ensure that the significantly higher maxTokens value of 128000 and the knowledgeCutOff date of 2023-12-01 are consistent with the model's specifications and intended use cases. Additionally, consider documenting the rationale behind these specific values within the code or project documentation for future reference and clarity.
• 32-32: The logic for calculating requestTokens by subtracting responseTokens and an additional 100 tokens from maxTokens is applied universally after the switch statement. This approach is consistent and maintains simplicity in the token management logic. However, it's important to verify that this margin of 100 tokens is sufficient and appropriate for all models, especially considering the significant increase in maxTokens for the gpt-4-turbo-preview model. If the margin is intended to account for overhead or ensure a buffer, it may be worth evaluating if a proportional margin relative to the maxTokens would be more appropriate for models with vastly different token limits.

[pascal-zarrad]

This PR was meant for a fork. It can be ignored and has been closed.