import { ILlmSchema } from "./ILlmSchema";
import { IValidation } from "./IValidation";
/**
 * LLM function metadata.
 *
 * `ILlmFunction` is an interface representing a function metadata,
 * which has been used for the LLM (Language Large Model) function
 * calling. Also, it's a function structure containing the function
 * {@link name}, {@link parameters} and {@link output return type}.
 *
 * If you provide this `ILlmFunction` data to the LLM provider like "OpenAI",
 * the "OpenAI" will compose a function arguments by analyzing conversations
 * with the user. With the LLM composed arguments, you can execute the function
 * and get the result.
 *
 * By the way, do not ensure that LLM will always provide the correct
 * arguments. The LLM of present age is not perfect, so that you would
 * better to validate the arguments before executing the function.
 * I recommend you to validate the arguments before execution by using
 * [`typia`](https://github.com/samchon/typia) library.
 *
 * @reference https://platform.openai.com/docs/guides/function-calling
 * @author Jeongho Nam - https://github.com/samchon
 */
export interface ILlmFunction<Model extends ILlmSchema.Model> {
    /**
     * Representative name of the function.
     *
     * @maxLength 64
     */
    name: string;
    /**
     * List of parameter types.
     */
    parameters: ILlmSchema.ModelParameters[Model];
    /**
     * Collection of separated parameters.
     */
    separated?: ILlmFunction.ISeparated<Model>;
    /**
     * Expected return type.
     *
     * If the function returns nothing (`void`), the `output` value would
     * be `undefined`.
     */
    output?: ILlmSchema.ModelSchema[Model];
    /**
     * Description of the function.
     *
     * For reference, the `description` is very important property to teach
     * the purpose of the function to the LLM (Language Large Model), and
     * LLM actually determines which function to call by the description.
     *
     * Also, when the LLM conversates with the user, the `description` is
     * used to explain the function to the user. Therefore, the `description`
     * property has the highest priority, and you have to consider it.
     */
    description?: string | undefined;
    /**
     * Whether the function is deprecated or not.
     *
     * If the `deprecated` is `true`, the function is not recommended to use.
     *
     * LLM (Large Language Model) may not use the deprecated function.
     */
    deprecated?: boolean | undefined;
    /**
     * Category tags for the function.
     *
     * You can fill this property by the `@tag ${name}` comment tag.
     */
    tags?: string[] | undefined;
    /**
     * Validate function of the arguments.
     *
     * You know what? LLM (Large Language Model) like OpenAI takes a lot of
     * mistakes when composing arguments in function calling. Even though
     * `number` like simple type is defined in the {@link parameters} schema,
     * LLM often fills it just by a `string` typed value.
     *
     * In that case, you have to give a validation feedback to the LLM by
     * using this `validate` function. The `validate` function will return
     * detailed information about every type errors about the arguments.
     *
     * And in my experience, OpenAI's `gpt-4o-mini` model tends to construct
     * an invalid function calling arguments at the first trial about 50% of
     * the time. However, if correct it through this `validate` function,
     * the success rate soars to 99% at the second trial, and I've never
     * failed at the third trial.
     *
     * > If you've {@link separated} parameters, use the
     * > {@link ILlmFunction.ISeparated.validate} function instead when
     * > validating the LLM composed arguments.
     * >
     * > In that case, This `validate` function would be meaningful only
     * > when you've merged the LLM and human composed arguments by
     * > {@link HttpLlm.mergeParameters} function.
     *
     * @param args Arguments to validate
     * @returns Validation result
     */
    validate: (args: unknown) => IValidation<unknown>;
}
export declare namespace ILlmFunction {
    /**
     * Collection of separated parameters.
     */
    interface ISeparated<Model extends ILlmSchema.Model> {
        /**
         * Parameters that would be composed by the LLM.
         *
         * Even though no property exists in the LLM side, the `llm` property
         * would have at least empty object type.
         */
        llm: ILlmSchema.ModelParameters[Model];
        /**
         * Parameters that would be composed by the human.
         */
        human: ILlmSchema.ModelParameters[Model] | null;
        /**
         * Validate function of the separated arguments.
         *
         * If LLM part of separated parameters has some properties,
         * this `validate` function will be filled for the {@link llm}
         * type validation.
         *
         * > You know what? LLM (Large Language Model) like OpenAI takes a lot of
         * > mistakes when composing arguments in function calling. Even though
         * > `number` like simple type is defined in the {@link parameters} schema,
         * > LLM often fills it just by a `string` typed value.
         * >
         * > In that case, you have to give a validation feedback to the LLM by
         * > using this `validate` function. The `validate` function will return
         * > detailed information about every type errors about the arguments.
         * >
         * > And in my experience, OpenAI's `gpt-4o-mini` model tends to construct
         * > an invalid function calling arguments at the first trial about 50% of
         * > the time. However, if correct it through this `validate` function,
         * > the success rate soars to 99% at the second trial, and I've never
         * > failed at the third trial.
         *
         * @param args Arguments to validate
         * @returns Validate result
         */
        validate?: ((args: unknown) => IValidation<unknown>) | undefined;
    }
}