Token Optimization: Config-Driven Compression For Enhanced Efficiency

Hey everyone! 👋 Let's dive into something super cool that's going to seriously boost how our token optimization operations run. We're talking about implementing configuration-driven thresholds, just like the awesome folks over at Gemini CLI have done. This means we're moving away from those pesky hardcoded values scattered all over the place and embracing a centralized configuration system that's got schema validation baked right in. Ready to level up our game? Let's go! 🚀

The Problem: Hardcoded Values & Why They're a Headache 🤕

Alright, so here's the deal: currently, we've got hardcoded values all over our codebase. These values control things like when we start compressing tokens and how much we preserve during compression. The problem with this approach, though, is that it's a real pain to change these settings. Every time we want to tweak something, we have to dig into the code, make the changes, and redeploy. This is not only time-consuming but also prone to errors. Plus, it makes it super difficult to experiment with different settings to find the sweet spot for optimal performance. We want to be able to easily adjust these thresholds without having to touch a single line of code, and that’s where the magic of configuration-driven thresholds comes in. Think of it as giving ourselves the power to fine-tune our token optimization process on the fly! 💪

We need a more flexible, maintainable, and efficient way to manage these settings, and that's where configuration-driven compression comes in to save the day!

The Gemini CLI Inspiration 💡

We're taking inspiration from the Gemini CLI, which uses a centralized configuration system. It also uses a schema for configuration validation. Let's take a look at the file packages/core/src/config/settingsSchema.ts. This configuration includes:

• CompressionTokenThreshold: The default is set to 0.7 (or 70%).
• CompressionPreserveThreshold: The default is set to 0.3 (or 30%).
• Also defines limits for tokenBudget on a per-model basis.

The Solution: Configuration-Driven Thresholds 🛠️

So, what's the plan, guys? We're going to create a configuration file that holds all these important settings. This file will live in a special place on your system, and it'll be loaded up every time our token optimization operations run. This way, we can easily change the behavior of the system without having to modify the code. And the best part? We'll include schema validation to make sure our configuration is always valid and consistent. No more guesswork or accidental errors! 🎉

The Configuration File: Your New Best Friend 📝

We're aiming to create a configuration file, let's say ~/.token-optimizer/config.json. This file will act as the central hub for all the configuration options that drive how token optimization works. It will contain settings like compressionTokenThreshold, compressionPreserveThreshold, minTokensBeforeCompression, and much more. This means you will have a good grip on how the program works and the ability to customize your experience. Here's a sneak peek at what this file might look like:

{
  "compressionTokenThreshold": 0.7,
  "compressionPreserveThreshold": 0.3,
  "minTokensBeforeCompression": 1000,
  "modelTokenLimits": {
    "gemini-2.5-flash": 1000000,
    "gemini-1.5-pro": 2000000
  },
  "cacheSettings": {
    "maxSize": 1000,
    "ttlSeconds": 3600
  },
  "optimizationSettings": {
    "minOutputSizeBytes": 500,
    "quality": "balanced"
  }
}

This config.json file will house settings like compressionTokenThreshold, which tells us the threshold at which we trigger compression (e.g., compress when we hit 70% of the token limit). It also has compressionPreserveThreshold, defining how much of the original tokens we want to keep after compression (e.g., preserve 30%). Besides these, there are other settings to play with, like minTokensBeforeCompression, which tells the system when to start compression. You can also set modelTokenLimits for specific models. Finally, we can also configure cache and optimization settings.

PowerShell Config Class 💻

To make this all work, we'll need a PowerShell Config class in src/Config.ps1. This class will handle loading and saving the configuration file. It will also validate the configuration to make sure it's correct. This means you can create, load, and change settings without having to dive into the core code.

Acceptance Criteria: What Success Looks Like ✅

Here's what we need to get done to consider this a win:

• Configuration Schema with Validation: We need to define a schema (likely using JSON schema) to ensure our configuration file is valid. This will prevent errors and make sure that we're always using correct settings.
• PowerShell Config Class with Load/Save: We'll create a PowerShell class to handle loading the config, saving it, and making it easy to access within our scripts.
• Default Config Created on First Run: When the system is first used, a default configuration file should be created automatically. This ensures that the system works out-of-the-box.
• Orchestrator Uses Config Values: The core optimization process (the