langflow-toon 0.1.1

TOON (Token-Oriented Object Notation) converter component for Langflow

TOON Converter for Langflow

A custom Langflow component that converts structured data (JSON, XML, CSV, HTML) into TOON (Token-Oriented Object Notation) format to optimize token usage for LLM prompts.

Features

• Multi-format Support: Convert JSON, XML, CSV, and HTML to TOON format
• Token Optimization: Achieve 30%+ token reduction compared to formatted JSON
• Configurable Delimiters: Choose between comma, tab, or pipe separators
• Token Statistics: View before/after token counts and savings percentage
• Memory Warnings: Get alerts for large datasets before processing
• Detailed Error Messages: Get location-specific error messages with corrected examples

Installation

From Source

git clone https://github.com/faisolp/langflow-toon-component.git
cd toon
pip install -e .

Dependencies

pip install -r requirements.txt

Required packages:

• tiktoken>=0.5.0 - Token counting for optimization analysis
• xmltodict>=0.13.0 - XML parsing and conversion
• pytest>=7.0.0 - Testing framework (development only)
• pytest-cov>=4.0.0 - Test coverage reporting (development only)

Usage

Python API Usage

from langflow_toon import ToonConverter
from langflow_toon.models.data import ConversionConfig, Delimiter

# Initialize converter
converter = ToonConverter()

# Convert JSON to TOON
json_data = '{"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}'
result = converter.convert(json_data, input_format='JSON')

print(result.toon_output)
# Output:
# users[2]{id,name}:
#   1,Alice
#   2,Bob

print(f"Token reduction: {result.token_reduction} ({result.token_reduction/result.original_tokens*100:.1f}%)")

Basic Usage in Langflow

1. Add the "TOON Converter" component to your Langflow flow
2. Connect your data source to the input_text port
3. Configure the input_format (or enable auto_detect for automatic detection)
4. Choose your preferred csv_delimiter (comma, tab, or pipe)
5. Toggle sort_keys to alphabetically sort object keys
6. Toggle ensure_ascii to encode non-ASCII characters
7. Connect the toon_output to your LLM prompt
8. View original_tokens, toon_tokens, and token_reduction statistics

Example: JSON to TOON

Input (JSON):

{
  "users": [
    {"id": 1, "name": "Alice", "role": "admin"},
    {"id": 2, "name": "Bob", "role": "user"}
  ]
}

Output (TOON):

users[2]{id,name,role}:
  1,Alice,admin
  2,Bob,user

Token Savings: 85 → 42 tokens (50.6% reduction)

Example: CSV with Tab Delimiter

Input (CSV):

name,age,city
Alice,30,NYC
Bob,25,LA

Output (TOON with tab delimiter):

data[2 ]{name	age	city}:
  Alice	30	NYC
  Bob	25	LA

Example: Complete Langflow Workflow

[HTTP Request] → [TOON Converter] → [LLM Prompt]
                    ↓
              [Token Stats Display]

Flow Configuration:

1. HTTP Request: Fetch JSON data from an API
2. TOON Converter:
   • input_text: Connect from HTTP Request output
   • input_format: JSON
   • csv_delimiter: comma (default)
   • auto_detect: false (format known)
   • sort_keys: false (preserve order)
   • ensure_ascii: false (keep Unicode)
3. LLM Prompt: Use {{toon_output}} variable in prompt template
4. Token Stats Display: Show {{original_tokens}}, {{toon_tokens}}, {{token_reduction}}

Example: Multi-Format Processing Pipeline

                    ┌─────────────┐
                    │ Format      │
                    │ Detector    │
                    └──────┬──────┘
                           │
          ┌────────────────┼────────────────┐
          │                │                │
     ┌────▼────┐     ┌────▼────┐     ┌────▼────┐
     │   JSON  │     │   XML   │     │   CSV   │
     │ Source  │     │ Source  │     │ Source  │
     └────┬────┘     └────┬────┘     └────┬────┘
          │               │               │
          └───────────────┼───────────────┘
                          │
                   ┌──────▼──────┐
                   │  TOON       │
                   │  Converter  │
                   └──────┬──────┘
                          │
                   ┌──────▼──────┐
                   │  LLM        │
                   │  Processing │
                   └─────────────┘

Component API

Inputs

Port	Type	Description
input_data	string	Raw data to convert (JSON, XML, CSV, or HTML)
input_format	enum	Data format: AUTO, JSON, XML, CSV, HTML
delimiter	enum	TOON field delimiter: COMMA, TAB, PIPE
sort_keys	boolean	Sort object keys alphabetically (default: false)

Outputs

Port	Type	Description
toon_output	string	TOON formatted data
token_stats	object	Token usage statistics
error	string	Error message (if conversion fails)

Development

Running Tests

# Run all tests
pytest tests/

# Run unit tests only
pytest tests/unit/

# Run with coverage
pytest --cov=langflow_toon tests/

Project Structure

langflow_toon/
├── __init__.py
├── component.py         # Main Langflow custom component
├── converters/          # Format-specific converters
├── validators/          # Input and TOON validation
├── utils/               # Token counting, memory estimation, error formatting
└── config/              # Constants and configuration

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details

Links

• TOON Format Specification
• Langflow Documentation