Its tabular and indented mother nature mimics formats these versions are by now trained on, making it a great choice for prompting and information retrieval tasks.
TOON addresses quite a few significant issues inherent in JSON when utilized with LLMs. JSON's verbosity and redundant syntax, characterized by recurring keys and in depth punctuation, cause inefficient tokenization and inflated token counts. For instance, in an variety of objects, keys like "id" and "title" are recurring For each entry, consuming beneficial context window Area.
Sure, LLMs can have an understanding of TOON format similar to common JSON. The structure maintains the same construction and facts, just in a far more compact illustration. You could include a crucial mapping Initially if applying short keys.
One of the better things about fashionable JavaScript tooling is that you usually needn't "put in" anything at all to start out. If you just desire to check out TOON out on just one file, you can use npx to run the binary right:
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Not enough Schema Enforcement: JSON lacks a constructed-in schema, forcing builders to explain ideal knowledge structures and kinds JSON TO TOON in the prompt by itself. This consumes precious context window House and increases the likelihood of LLMs building non-compliant output.
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Token Effectiveness: Arrays of objects are converted to JSON2TOON some tabular format with only one header row, eradicating repetitive keys.
Esta es la característica más poderosa de TOON. Cuando tienes un array de objetos con la misma estructura (como listas de usuarios, productos, o transacciones), TOON convierte estos datos en un formato tabular donde las claves se definen una sola vez. Esto elimina la redundancia masiva que existe en JSON y puede reducir el tamaño hasta en un 70%.
Regardless if you are debugging a prompt, getting ready a dataset, or just inquisitive about just how much funds you could possibly preserve on tokens, the @toon-format/toon CLI is your new ally.
Combina TOON con técnicas de prompt engineering. En lugar 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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This tool is ideal for builders, scientists, and corporations wanting to improve their LLM interactions, make improvements to data readability, and regulate API fees properly even though retaining rigorous knowledge privateness.
TOON structure utilizes limited vital abbreviations, eliminates needless whitespace, and optimizes the JSON framework to minimize token rely when preserving all facts. This can reduce token use by 30-60% in contrast to standard JSON.