toon-format/toon

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.

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