Reorganiza tus objetos para maximizar el uso de tablas TOON. Si tienes un objeto con múltiples arrays, considera separar cada array en su propia tabla TOON.
TOON addresses a number of essential issues inherent in JSON when employed with LLMs. JSON's verbosity and redundant syntax, characterised by repeated keys and intensive 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 useful context window space.
If you should convert this back to entire JSON afterwards, you can use the --expand-paths safe flag throughout decoding to reconstruct the deep object structure perfectly.
It simplifies knowledge serialization for AI workflows, making it a vital asset in the evolving landscape of huge language styles.
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Not enough Schema Enforcement: JSON lacks a designed-in schema, forcing developers to explain wanted information buildings and types throughout the prompt by itself. This consumes beneficial context window House and boosts the chance of LLMs creating non-compliant output.
The CLI permits you to swap delimiters over the fly. In case you are working with tabular data, switching to some tab delimiter could make the output glimpse cleaner and process a lot more proficiently.
But wherever the CLI definitely proves its worth is from the "Unix philosophy"—tiny instruments loosely joined. Since the TOON CLI reads from typical enter (stdin) and writes to plain output (stdout), you may TOON formater pipe facts right by it.
The @toon-structure/cli is much more than just a file converter; it is a utility belt with the LLM period. By shifting info processing on the command line, it is possible to iterate more quickly, visualize optimizations immediately, and combine token-successful formats into your existing engineering workflows.
This Device is perfect for developers, researchers, and everyone working with LLMs who should improve their API usage and deal with costs efficiently whilst protecting information privacy. It offers a practical Remedy to your difficulties posed by JSON's verbosity and rigidity in AI contexts.
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.
Syntactic Rigidity: JSON is very sensitive to insignificant problems like misplaced commas or quotes. LLMs can certainly introduce these kinds of errors, necessitating strong validation and parsing logic in programs.
This Resource is perfect for builders, researchers, and firms aiming to improve their LLM interactions, improve information readability, and control API fees efficiently although retaining rigid facts privateness.
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