caio-atharva 1.0.11

Minify LLM prompts to reduce API token costs

CAIO (Cost-Aware Inference Orchestration)

CAIO (Cost-Aware Inference Orchestration) is a prompt-optimization library designed to reduce LLM API token usage while preserving developer intent and code semantics.

CAIO applies deterministic transformations and lightweight heuristics to:

• remove conversational noise (greetings, politeness, intros)
• strip long docstrings and comments
• normalize whitespace and punctuation
• abbreviate common stop-phrases and optionally vowel-strip long words

The output is a compact, instruction-focused prompt optimized for lower token counts and lower inference cost.

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Key Features

• Generalized NLP Scrubber: identifies and removes non-essential conversational text while preserving code and intent.
• Code Minifier: strips comments, unnecessary whitespace and formatting overhead.
• Tiered Optimization: choose aggressive (aggressive), development (dev) or conservative (prod) tiers.
• Deterministic, Non-destructive: preserves code tokens and avoids changing program semantics.
• Fast and Local: runs locally with no external dependencies required for optimization itself.

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Installation

pip install caio-atharva

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Usage

Import CAIO, initialize it, and call optimize() to transform verbose prompts into compact instructions.

Example — Fibonacci (tested locally)

from caio import CAIO

optimizer = CAIO()

bloated_prompt = '''
Hello there! I hope you are having a great day.
I'm new to Python and I was wondering if you could please help me.
Could you write a function to calculate the fibonacci sequence?
Make sure it's recursive. Thanks so much!
'''

result = optimizer.optimize(bloated_prompt, tier="dev")

print("Original length:", len(bloated_prompt))
print("Optimized prompt:\n", result["optimized_prompt"])
print("Tokens before:", result.get("tokens_before"), "tokens after:", result.get("tokens_after"))

Expected optimized prompt (example):

CODE-ONLY;NO-CHAT;MINIFY-RESPONSE: write a function to calculate the fibonacci sequence; make it recursive

Notes:

• The optimizer preserves code-like tokens (def, return, import, =) and prioritizes retaining program logic.
• Use tier='dev' for more aggressive reductions during development; prod is safer for production workloads.

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Project Structure

CAIO-SDK/
├── caio/
│   ├── __init__.py
│   └── optimizer.py  # Core logic: CAIO class & NLP scrubber
├── setup.py          # Package configuration
└── README.md         # Documentation

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Metadata

• Developer: Atharva Matale
• License: MIT
• Version: 1.0.10
• Version: 1.0.11

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Maximize efficient inference. Minimize costs. Use CAIO.