Ensure uniform application of trimTokens in underlying LLM calls

[monilpat]

Describe the bug

The trimTokens function is inconsistently applied across different underlying LLM calls, leading to errors when the context window is exceeded.

To Reproduce

1. Call the LLM function in various parts of the codebase.
2. Observe that in some instances, trimTokens is applied, while in others, it is not.
3. Execute a call that exceeds the context window limit to trigger an error.

Expected behavior

The trimTokens function should be uniformly applied across all LLM calls to prevent exceeding the context window and causing errors.

Additional context

Uniform application of trimTokens will help maintain consistency and avoid errors related to context window limits.

Related Issues

[AIFlowML]

I will look this.

[AIFlowML]

Some tips about this

Comprehensive Solution for Uniform Application of trimTokens

1. TLDR (Executive Summary)

• Issue Essence: Inconsistent application of trimTokens across LLM calls leads to context window errors.
• Root Cause: trimTokens is not uniformly applied in all LLM calls.
• Solution Approach: Ensure trimTokens is applied consistently across all LLM calls.
• Impact Assessment: Prevents context window errors, improves code consistency, and enhances performance.

2. Quick Resolution Path

• Immediate Steps:
  1. Locate the trimTokens function in packages/core/src/generation.ts.
  2. Identify all LLM calls in the codebase.
  3. Apply trimTokens uniformly in each LLM call.
• Exact File Locations and Line Numbers:
  • packages/core/src/generation.ts (Line 308)
• Required Code Changes:

  // Example LLM call without trimTokens
  const response = llmCall(input);
  
  // Modified LLM call with trimTokens
  const trimmedInput = trimTokens(input);
  const response = llmCall(trimmedInput);

• Validation Commands:
  • Run unit tests to ensure no context window errors occur.
  • Test each function that makes LLM calls.
• Rollback Procedures:
  • Revert the changes if errors are introduced.
  • Use version control to track changes.

3. Comprehensive Solution

• Detailed Technical Analysis:
  • The trimTokens function truncates the context to the maximum length allowed by the model. Inconsistent application leads to errors when the context window is exceeded.
• Step-by-Step Implementation:
  1. Review packages/core/src/generation.ts to locate trimTokens.
  2. Identify all functions that make LLM calls.
  3. Apply trimTokens before each LLM call.
  4. Test the application to ensure no context window errors occur.
• Dependencies and Side Effects:
  • No additional dependencies required.
  • Ensure trimTokens does not truncate important data.
• Configuration Changes:
  • No configuration changes needed.
• Environment Requirements:
  • Ensure the environment supports the updated code.
• Database Changes:
  • No database changes required.
• Service Impacts:
  • Improved stability and performance of LLM calls.