data-formats

TOON encoding reduces JSON token usage for LLM communication by 40-60%. By encoding Python dictionaries into TOON format, you fit more information within token limits, ensuring faster and more cost-effective LLM processing.

High-performance JSON serialization handles large datasets through streaming and batching. By utilizing ultra-fast libraries like orjson, you can efficiently process and serialize massive JSON files without exhausting system memory.

Data validation validates YAML and JSON structures using schema management and type checking. It implements robust error handling using libraries like jsonschema, cerberus, and marshmallow to ensure data integrity.

orjson and PyYAML are supported for high-performance data serialization. They handle JSON and YAML formats respectively, enabling ultra-fast encoding, streaming, and efficient processing of complex data structures.

TOON encoding is needed for LLM optimization when standard JSON exceeds token limits. It achieves 40-60% token reduction, allowing you to transmit complex data structures to LLMs more efficiently and cost-effectively.

Streaming large datasets with orjson and ijson is limited by the need for proper batching and schema validation. While it enables efficient memory usage, complex nested structures may require advanced type checking to prevent processing errors.