fix(chat): use estimateTokens helper for chat token calculation

[steebchen]

Replace manual token calculations with the estimateTokens helper function for consistent and accurate estimation.

Previously, some token estimations relied on a simple division by 4, which is less accurate and inconsistent with other parts of the codebase. This PR leverages the existing estimateTokens function, which uses proper tokenization and a more robust fallback strategy, ensuring a unified and more precise approach to token counting across the application.

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Summary by CodeRabbit

• Refactor
  • Improved consistency and accuracy of token usage estimates across chat providers by centralizing token estimation logic.

[coderabbitai]

Walkthrough

This change refactors token count estimation in the chat module by replacing all manual and duplicated logic with calls to a centralized estimateTokens helper function. All previous direct encoding, length heuristics, and try-catch blocks for token estimation are removed, delegating token counting to the shared function for consistency.

Changes

Cohort / File(s)	Change Summary
Token Estimation Refactor apps/gateway/src/chat/chat.ts	Replaces all manual token estimation (encoding, length heuristics, try-catch) with calls to estimateTokens in all relevant locations; unifies logic for prompt and completion token calculation across providers.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

Possibly related PRs

• fix(chat): ensure usage data is included in streaming #318: Both PRs modify token usage handling in streaming chat completions and involve fallback token estimation, but this PR centralizes the logic while fix(chat): ensure usage data is included in streaming #318 ensures usage data inclusion and validation.
• feat(chat): simplify Google streaming data parsing #536: Both PRs touch token estimation in apps/gateway/src/chat/chat.ts; this PR generalizes and consolidates logic, while feat(chat): simplify Google streaming data parsing #536 focuses on Google streaming response parsing and fallback estimation.
• fix(chat): remove unused Anthropic token estimation logic #385: Both address Anthropic token estimation; this PR replaces manual logic with a unified function, while fix(chat): remove unused Anthropic token estimation logic #385 removes a now-unused Anthropic-specific estimation block.

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Reviewing files that changed from the base of the PR and between 8fd91fe and 9c28a5e.

📒 Files selected for processing (1)

• apps/gateway/src/chat/chat.ts (5 hunks)

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📓 Path-based instructions (4)

**/*.{js,jsx,ts,tsx}

📄 CodeRabbit Inference Engine (.github/copilot-instructions.md)

Use localStorage instead of cookies for client-side data persistence

Files:

• apps/gateway/src/chat/chat.ts

**/*.{js,ts}

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**/*.{js,ts}: Use drizzle with the latest object syntax for database operations
For read queries, always use db().query.<table>.findMany() or db().query.<table>.findFirst()

Files:

• apps/gateway/src/chat/chat.ts

{apps/api,apps/gateway,packages/db}/**/*.ts

📄 CodeRabbit Inference Engine (CLAUDE.md)

{apps/api,apps/gateway,packages/db}/**/*.ts: Use Drizzle ORM with latest object syntax for database operations
For reads, use db().query.<table>.findMany() or db().query.<table>.findFirst()

Files:

• apps/gateway/src/chat/chat.ts

**/*.{ts,tsx}

📄 CodeRabbit Inference Engine (.cursor/rules/general.mdc)

Never use as any or : any in TypeScript files.

Files:

• apps/gateway/src/chat/chat.ts

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• GitHub Check: e2e / run

🔇 Additional comments (5)

apps/gateway/src/chat/chat.ts (5)

168-176: LGTM! Good refactoring to use centralized token estimation.

The replacement of manual token calculation with the estimateTokens helper function improves consistency and removes duplicated try-catch logic. The parameters are correctly set with an empty messages array since only completion tokens are being estimated from content.

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362-370: LGTM! Consistent refactoring pattern.

This change maintains the same improvement as the previous segment, replacing manual token estimation with the centralized estimateTokens helper. The parameter usage is consistent and appropriate for estimating completion tokens from streaming content.

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2099-2107: LGTM! Proper prompt token estimation.

The refactoring correctly uses estimateTokens to calculate prompt tokens from the messages array when streaming data indicates completion. The function call parameters and result extraction are appropriate for this use case.

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2183-2191: LGTM! Consistent Anthropic provider token estimation.

The refactoring properly applies the centralized token estimation approach to Anthropic streaming usage data. The variable naming and parameter usage are clear and consistent with the overall refactoring pattern.

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2301-2309: LGTM! Completes the token estimation refactoring.

This final replacement of manual token calculation with the estimateTokens helper completes the consistent refactoring throughout the streaming response handling. All token estimation logic is now centralized and unified across different providers and scenarios.

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