toon-format/toon
Toon
Toon is a data serialization library and toolkit designed to convert complex objects into compact, human-readable formats optimized for large language models. By focusing on token efficiency, the library minimizes the context window footprint of structured data through techniques like key folding and tabular layout optimization. It provides a streaming-capable processor that handles the encoding and decoding of hierarchical data while maintaining structural integrity.
The project distinguishes itself through its path-aware transformation pipeline and configurable serialization logic, which allow for precise control over how data is represented. It supports advanced features such as dotted path expansion, custom delimiter styles, and the normalization of complex data types like dates and maps. These capabilities enable developers to adapt serialized output to specific system requirements while ensuring consistent parsing behavior across different environments.
Beyond core serialization, the library includes a suite of developer-facing tools for data format conversion, schema validation, and editor integration. It also provides diagnostic utilities to analyze and compare token counts, helping users measure the efficiency of their data structures. The framework is built to handle large datasets incrementally through event-driven stream processing, ensuring memory efficiency even when working with massive records.
Features
- Serialization Libraries - Provides a toolkit for converting complex objects into space-optimized, human-readable formats.
- Token Optimization Utilities - Combines indentation and tabular layouts to minimize token usage for large language model inputs.
- Data Serialization - Encodes objects and arrays into compact, indentation-based formats optimized for language model token efficiency.
- Data Serialization Formats - Defines a compact, token-efficient data representation specifically designed for large language model context windows.
- Path-Folding Serializers - Collapses nested hierarchies into dotted key paths to reduce token consumption for language models.
- Structured Data Parsers - Ships a streaming-capable processor that handles encoding and decoding of hierarchical data structures.
- Data Serialization Formats - Converts complex objects into compact, human-readable formats for efficient storage and transmission.
- Model Output Formatting - Converts tool outputs into compact, token-efficient structures for language model ingestion.
- Data Format Interoperability - Transforms data between standard formats and compact representations to facilitate efficient data processing pipelines.
- Data Persistence - Maps objects to compact, serialized formats to enable consistent data persistence across different programming environments.
- Deserialization Engines - Converts formatted strings back into native objects while expanding paths and resolving data conflicts.
- Data Validation Libraries - Enforces schema consistency and detects data corruption by checking serialized documents against structural invariants.
- Incremental Data Streaming - Handles massive data records incrementally through event-driven stream processing to maintain memory efficiency.
- Nested Serializers - Reduces token usage by folding chains of single-key objects into concise dotted path representations.
- Development Utilities - Compact, deterministic JSON format for structuring LLM prompts.
- Data Persistence and Storage - Persists information in an embedded, key-value compatible database using compact serialization formats.
- Schema-Validated Data Structures - Ensures data integrity and schema conformance through strict structural checks during encoding and decoding.
- Streaming Processors - Handles massive datasets through event-driven stream processing to ensure memory efficiency.
- Stream Processing Utilities - Processes formatted input as a sequence of events to enable memory-efficient handling of large datasets.
- Data Schema Validation - Enforces data integrity and consistent parsing using schema-based validation and length metadata.
- Runtime Path Resolvers - Transforms flattened key paths into nested object structures to support deep-merge semantics during decoding.
- Data Type Mappings - Maps complex programming types like dates and maps into standard formats to ensure consistent cross-environment output.
- Object-Relational Mapping Utilities - Provides an interface for interacting with databases using object-oriented patterns to simplify data persistence.
- Tabular Layout Optimizers - Structures arrays of objects into column-aligned formats to reduce key overhead and improve parsing.
- Path-Aware Transformers - Applies custom filtering and modification logic during serialization by tracking key paths.
- Lenient Parsers - Parses malformed or non-standard data by relaxing validation rules to prevent conversion failures.
- Delimited Data Parsers - Provides configurable delimiter logic for serializing and parsing text-based data formats.
- Data Transformation - Filters and modifies data values during serialization using path-aware logic for precise output control.
- Tabular Data Formatters - Structures arrays of objects into space-efficient, column-aligned tabular layouts to improve parsing performance.
- Custom Serializer Fields - Enables custom logic for converting unique data types by overriding default serialization rules.
