redukon 3.4.1

Simple CLI tool for AI text rewriting with Ollama

Redukon

Reduce Your Tokens. Keep Your Intent.

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Table of Contents

• What is Redukon?
• Quick Start
• CLI Usage
  • redukon onboard
  • redukon rewrite
  • redukon serve
• API Usage
  • Non-Streaming Example
  • Streaming Example
  • API Parameters
• Docker
• Batch Mode
• Architecture
• Example
• Options
• Requirements
• License

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What is Redukon?

Redukon is a token-saving prompt rewriter that uses local small AI models to optimize your prompts — reducing token count while keeping the core intent intact.

No API keys needed. Runs entirely offline with Ollama.

Perfect for:

• Saving money on API calls
• Fitting more context into LLM windows
• Making prompts more efficient
• Building into your own apps via API

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Quick Start

# Install from PyPI
pip install redukon

# Or install latest from GitHub
pip install -e https://github.com/CharlesArea/Redukon.git

# One-time setup (installs Ollama + picks a model)
redukon onboard

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CLI Usage

redukon onboard

Interactive setup wizard:

1. Checks if Ollama is installed
2. Installs Ollama if needed
3. Choose your model:

   Model	Size	Best For
   qwen2.5:0.5b	397MB	Speed & minimal resources
   llama3.2:1b	1.9GB	Balanced
   phi3:3.8b	2.3GB	Better quality

redukon rewrite

Rewrite your prompts from command line:

# Basic
redukon rewrite -i "Please write a comprehensive Python function..."

# From file
redukon rewrite -i @long-prompt.txt -o optimized.txt

# Custom temperature (lower = more focused)
redukon rewrite -i "prompt" --temp 0.3

redukon serve

Start the API server:

# Default port 8000
redukon serve

# Custom port
redukon serve --port 9000
redukon serve --host 127.0.0.1 --port 8080

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API Usage

Start Server

redukon serve

Endpoints

Endpoint	Method	Description
/rewrite	POST	Rewrite a prompt (non-streaming)
/rewrite/stream	POST	Rewrite a prompt (streaming)
/health	GET	Health check

Non-Streaming Example

curl -X POST http://localhost:8000/rewrite \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Please write a comprehensive Python function that takes a list of integers...",
    "model": "qwen2.5:0.5b",
    "temperature": 0.3
  }'

Streaming Example

curl -X POST http://localhost:8000/rewrite/stream \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Your prompt here"}'

Response (SSE format):

data: {"chunk": "Write a "}
data: {"chunk": "Python function..."}
data: {"done": true, "original_tokens": 87, "optimized_tokens": 42, "saved_tokens": 45, "saved_percent": 52}

Example Response

{
  "optimized_prompt": "Write a Python function that filters even numbers from a list...",
  "original_tokens": 87,
  "optimized_tokens": 42,
  "saved_tokens": 45,
  "saved_percent": 52
}

API Parameters

Parameter	Type	Required	Description
prompt	string	Yes	The prompt to optimize
model	string	No	Model name (default: from config)
temperature	float	No	Temperature 0.0-1.0 (default: 0.3)

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Logging

API requests are logged to log/api-YYYY-MM-DD.log:

[2026-03-10 12:00:00] [REQUEST] input_length=250, model=qwen2.5:0.5b
[2026-03-10 12:00:05] [RESPONSE] output_length=120, original_tokens=62, optimized_tokens=30, saved_tokens=32, saved_percent=51%
[2026-03-10 12:00:05] [ERROR] Generation failed: Ollama not running

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Docker

Option 1: API Server Only (connects to Ollama on host)

# Build image
docker build -t redukon .

# Run (ensure Ollama is running on host)
docker run -d -p 8000:8000 --network host redukon

Option 2: Full Stack with docker-compose

# Start both Redukon API and Ollama
docker-compose up -d

# Stop
docker-compose down

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Batch Mode

Process multiple prompts at once!

CLI

# From file with multiple prompts (one per line)
redukon batch -i prompts.txt

# Multiple files
redukon batch -i @file1.txt @file2.txt

# With output
redukon batch -i prompts.txt -o results/
redukon batch -i prompts.txt -o results.json

API

curl -X POST http://localhost:8000/batch \
  -H "Content-Type: application/json" \
  -d '{
    "prompts": [
      "First prompt to optimize",
      "Second prompt to optimize",
      "Third prompt to optimize"
    ],
    "model": "qwen2.5:0.5b",
    "temperature": 0.3
  }'

Response:

{
  "results": [
    {"index": 0, "original": "...", "optimized_prompt": "...", "saved_tokens": 10, "saved_percent": 25},
    {"index": 1, "original": "...", "optimized_prompt": "...", "saved_tokens": 15, "saved_percent": 30}
  ],
  "summary": {
    "total": 2,
    "processed": 2,
    "total_original_tokens": 100,
    "total_saved_tokens": 25,
    "overall_saved_percent": 25
  }
}

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Redukon                              │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐   │
│  │   CLI       │    │   API       │    │   Batch     │   │
│  │  Commands   │    │  Server     │    │   Mode      │   │
│  └──────┬──────┘    └──────┬──────┘    └──────┬──────┘   │
│         │                   │                   │           │
│         └───────────────────┼───────────────────┘           │
│                             │                               │
│                    ┌────────▼────────┐                     │
│                    │   Ollama API     │                     │
│                    │  (local models)  │                     │
│                    └────────┬────────┘                     │
│                             │                               │
│                    ┌────────▼────────┐                     │
│                    │  Qwen/Llama/Phi │                     │
│                    │   0.5b - 4b     │                     │
│                    └─────────────────┘                     │
│                                                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │                  Logging                            │    │
│  │            log/api-YYYY-MM-DD.log                  │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │                  Config                             │    │
│  │            ~/.redukon/config.json                   │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

Components

Component	Description
CLI	Command-line interface for single prompt rewriting
API Server	Flask server with REST endpoints
Batch Mode	Process multiple prompts at once
Ollama	Local LLM runtime (connects to Ollama)
Models	Small models (qwen2.5:0.5b, llama3.2:1b, phi3:3.8b)
Logger	Date-based logging to log/ directory
Config	User settings in ~/.redukon/config.json

Data Flow

1. User Input → CLI / API / Batch
2. System Prompt → Loaded from config or default
3. Ollama API → Sends prompt to local model
4. Model Processing → Optimizes prompt (removes filler, shortens)
5. Response → Returns optimized prompt + token stats
6. Logging → Records request/response to log file

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Example

Before (87 tokens)	After (42 tokens)
"Please write a comprehensive Python function that takes a list of integers as input and returns a new list containing only the even numbers from the original list. The function should handle edge cases like empty lists, lists with no even numbers, and lists with negative numbers. Please include proper type hints, docstrings, and error handling."	"Write a Python function that filters even numbers from a list. Include type hints, error handling, and docstrings."

Savings: ~52%

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Options

Flag	Description	Default
-i, --input	Prompt or @file.txt	Required
-o, --output	Output file	Print to stdout
-m, --model	Override model	From config
-t, --temp	Temperature (0.0-1.0)	0.3

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Requirements

• Python 3.10+
• Ollama (installed automatically during onboard)

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License

MIT © 2026 CharlesArea

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Made with for the AI community

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