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TOON addresses quite a few important difficulties inherent in JSON when utilised with LLMs. JSON's verbosity and redundant syntax, characterised by repeated keys and comprehensive punctuation, bring about inefficient tokenization and inflated token counts. For example, in an assortment of objects, keys like "id" and "title" are repeated For each and every entry, consuming precious context window Room.
The CLI allows you to "fold" these nested keys into a single dot-notated path, flattening the hierarchy and preserving tokens on indentation and braces.
It simplifies information serialization for AI workflows, making it an important asset while in the evolving landscape of large language styles.
Within this workflow, you fetch the information, extract just the Lively users, convert it into a pipe-delimited TOON structure, and have a stats report on the number of tokens you only saved.
Pero por ahora, TOON es la mejor herramienta disponible para optimizar datos estructurados en aplicaciones de IA.
Should you feed it a .json file, it assumes you ought to encode it to TOON. In the event you supply a .toon file, it switches to decoding method to provide you with again JSON.
A diferencia de JSON, que fue creado para intercambio de datos entre aplicaciones World wide web, TOON está optimizado para reducir el consumo de tokens, lo que se traduce en ahorros TOON significativos tanto en costes de API como en capacidad de contexto.
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Los agentes de IA que toman decisiones basadas en múltiples fuentes de datos se benefician enormemente de TOON, ya que pueden procesar más información en su contexto de trabajo.
Combina TOON con técnicas de prompt engineering. En lugar TOON de incluir todos los datos, incluye un resumen en texto y los detalles completos en TOON solo cuando el LLM los necesite.
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The TOON CLI eradicates the guesswork While using the --stats flag. When encoding, this selection calculates the approximated token count and shows you the savings immediately. This is a must have if you find yourself budgeting for prime-volume LLM phone calls.
TOON structure works by using quick important abbreviations, gets rid of needless whitespace, and optimizes the JSON structure to attenuate token count though preserving all knowledge. This could cut down token utilization by thirty-60% as opposed to plain JSON.