typia/lib/llm.d.ts

Superfast runtime validators with only one line

import { ILlmApplication, ILlmController, ILlmSchema } from "@samchon/openapi";
/**
 * > You must configure the generic argument `Class`.
 *
 * TypeScript functions to LLM function calling controller.
 *
 * Creates a controller of LLM (Large Language Model) function calling from a
 * TypeScript class or interface type containing the target functions to be
 * called by the LLM function calling feature. The returned controller contains
 * not only the {@link application} of
 * {@link ILlmFunction function calling schemas}, but also the
 * {@link ILlmController.execute executor} of the functions.
 *
 * If you put the returned {@link ILlmController} to the LLM provider like
 * [OpenAI (ChatGPT)](https://openai.com/), the LLM will automatically select
 * the proper function and fill its arguments from the conversation (maybe
 * chatting text) with user (human). And you can actually call the function by
 * using {@link ILlmController.execute} property. This is the concept of the LLM
 * function calling.
 *
 * Here is an example of using `typia.llm.controller()` function for AI agent
 * development of performing such AI function calling to mobile API classes
 * through this `typia` and external `@agentica` libraries.
 *
 * ```typescript
 * import { Agentica } from "@agentica/core";
 * import typia from "typia";
 *
 * const agentica = new Agentica({
 *   model: "chatgpt",
 *   vendor: {
 *     api: new OpenAI({ apiKey: "********" }),
 *     model: "gpt-4o-mini",
 *   },
 *   controllers: [
 *     typia.llm.controller<ReactNativeFileSystem>(
 *       "filesystem",
 *       new ReactNativeFileSystem(),
 *     ),
 *     typia.llm.controller<ReactNativeGallery>(
 *       "gallery",
 *       new ReactNativeGallery(),
 *     ),
 *   ],
 * });
 * await agentica.conversate(
 *   "Organize photo collection and sort them into appropriate folders.",
 * );
 * ```
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template Class Target class or interface type collecting the functions to
 *   call
 * @template Config Configuration of LLM schema composition
 * @param name Identifier name of the controller
 * @param execute Executor instance
 * @param config Options for the LLM application construction
 * @returns Controller of LLM function calling
 * @reference https://wrtnlabs.io/agentica/docs/core/controller/typescript/
 */
export declare function controller(name: string, execute: object, config?: Partial<Pick<ILlmApplication.IConfig<any>, "separate" | "validate">>): never;
/**
 * TypeScript functions to LLM function calling controller.
 *
 * Creates a controller of LLM (Large Language Model) function calling from a
 * TypeScript class or interface type containing the target functions to be
 * called by the LLM function calling feature. The returned controller contains
 * not only the {@link application} of
 * {@link ILlmFunction function calling schemas}, but also the
 * {@link ILlmController.execute executor} of the functions.
 *
 * If you put the returned {@link ILlmController} to the LLM provider like
 * [OpenAI (ChatGPT)](https://openai.com/), the LLM will automatically select
 * the proper function and fill its arguments from the conversation (maybe
 * chatting text) with user (human). And you can actually call the function by
 * using {@link ILlmController.execute} property. This is the concept of the LLM
 * function calling.
 *
 * Here is an example of using `typia.llm.controller()` function for AI agent
 * development of performing such AI function calling to mobile API classes
 * through this `typia` and external `@agentica` libraries.
 *
 * ```typescript
 * import { Agentica } from "@agentica/core";
 * import typia from "typia";
 *
 * const agentica = new Agentica({
 *   model: "chatgpt",
 *   vendor: {
 *     api: new OpenAI({ apiKey: "********" }),
 *     model: "gpt-4o-mini",
 *   },
 *   controllers: [
 *     typia.llm.controller<ReactNativeFileSystem>(
 *       "filesystem",
 *       new ReactNativeFileSystem(),
 *     ),
 *     typia.llm.controller<ReactNativeGallery>(
 *       "gallery",
 *       new ReactNativeGallery(),
 *     ),
 *   ],
 * });
 * await agentica.conversate(
 *   "Organize photo collection and sort them into appropriate folders.",
 * );
 * ```
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template Class Target class or interface type collecting the functions to
 *   call
 * @template Config Configuration of LLM schema composition
 * @param name Identifier name of the controller
 * @param execute Executor instance
 * @param config Options for the LLM application construction
 * @returns Controller of LLM function calling
 * @reference https://wrtnlabs.io/agentica/docs/core/controller/typescript/
 */
export declare function controller<Class extends Record<string, any>, Config extends Partial<ILlmSchema.IConfig & {
    /**
     * Whether to disallow superfluous properties or not.
     *
     * If configure as `true`, {@link validateEquals} function would be used
     * for validation feedback, which is more strict than {@link validate}
     * function.
     *
     * @default false
     */
    equals: boolean;
}> = {}>(name: string, execute: Class, config?: Partial<Pick<ILlmApplication.IConfig<Class>, "separate" | "validate">>): ILlmController<Class>;
/**
 * > You must configure the generic argument `Class`.
 *
 * TypeScript functions to LLM function calling application.
 *
 * Creates an application of LLM (Large Language Model) function calling
 * application from a TypeScript class or interface type containing the target
 * functions to be called by the LLM function calling feature.
 *
 * If you put the returned {@link ILlmApplication.functions} objects to the LLM
 * provider like [OpenAI (ChatGPT)](https://openai.com/), the LLM will
 * automatically select the proper function and fill its arguments from the
 * conversation (maybe chatting text) with user (human). This is the concept of
 * the LLM function calling.
 *
 * By the way, there can be some parameters (or their nested properties) which
 * must be composed by human, not by LLM. File uploading feature or some
 * sensitive information like security keys (password) are the examples. In that
 * case, you can separate the function parameters to both LLM and human sides by
 * configuring the {@link ILlmApplication.IConfig.separate} property. The
 * separated parameters are assigned to the {@link ILlmFunction.separated}
 * property.
 *
 * For reference, the actual function call execution is not by LLM, but by you.
 * When the LLM selects the proper function and fills the arguments, you just
 * call the function with the LLM prepared arguments. And then informs the
 * return value to the LLM by system prompt. The LLM will continue the next
 * conversation based on the return value.
 *
 * Additionally, if you've configured {@link ILlmApplication.IConfig.separate},
 * so that the parameters are separated to human and LLM sides, you can merge
 * these human and LLM sides' parameters into one through
 * {@link HttpLlm.mergeParameters} before the actual LLM function call
 * execution.
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template Class Target class or interface type collecting the functions to
 *   call
 * @template Config Configuration of LLM schema composition
 * @param config Options for the LLM application construction
 * @returns Application of LLM function calling schemas
 * @reference https://platform.openai.com/docs/guides/function-calling
 */
export declare function application(config?: Partial<Pick<ILlmApplication.IConfig<any>, "separate" | "validate">>): never;
/**
 * TypeScript functions to LLM function calling application.
 *
 * Creates an application of LLM (Large Language Model) function calling
 * application from a TypeScript class or interface type containing the target
 * functions to be called by the LLM function calling feature.
 *
 * If you put the returned {@link ILlmApplication.functions} objects to the LLM
 * provider like [OpenAI (ChatGPT)](https://openai.com/), the LLM will
 * automatically select the proper function and fill its arguments from the
 * conversation (maybe chatting text) with user (human). This is the concept of
 * the LLM function calling.
 *
 * By the way, there can be some parameters (or their nested properties) which
 * must be composed by human, not by LLM. File uploading feature or some
 * sensitive information like security keys (password) are the examples. In that
 * case, you can separate the function parameters to both LLM and human sides by
 * configuring the {@link ILlmApplication.IConfig.separate} property. The
 * separated parameters are assigned to the {@link ILlmFunction.separated}
 * property.
 *
 * For reference, the actual function call execution is not by LLM, but by you.
 * When the LLM selects the proper function and fills the arguments, you just
 * call the function with the LLM prepared arguments. And then informs the
 * return value to the LLM by system prompt. The LLM will continue the next
 * conversation based on the return value.
 *
 * Additionally, if you've configured {@link ILlmApplication.IConfig.separate},
 * so that the parameters are separated to human and LLM sides, you can merge
 * these human and LLM sides' parameters into one through
 * {@link HttpLlm.mergeParameters} before the actual LLM function call
 * execution.
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template Class Target class or interface type collecting the functions to
 *   call
 * @template Config Configuration of LLM schema composition
 * @param config Options for the LLM application construction
 * @returns Application of LLM function calling schemas
 * @reference https://platform.openai.com/docs/guides/function-calling
 */
export declare function application<Class extends Record<string, any>, Config extends Partial<ILlmSchema.IConfig & {
    /**
     * Whether to disallow superfluous properties or not.
     *
     * If configure as `true`, {@link validateEquals} function would be used
     * for validation feedback, which is more strict than {@link validate}
     * function.
     *
     * @default false
     */
    equals: boolean;
}> = {}>(config?: Partial<Pick<ILlmApplication.IConfig<Class>, "separate" | "validate">>): ILlmApplication<Class>;
/**
 * > You must configure the generic argument `Parameters`.
 *
 * TypeScript parameters to LLM parameters schema.
 *
 * Creates an LLM (Large Language Model) parameters schema, a type metadata that
 * is used in the [LLM function
 * calling](https://platform.openai.com/docs/guides/function-calling) and [LLM
 * structured
 * outputs](https://platform.openai.com/docs/guides/structured-outputs), from a
 * TypeScript parameters type.
 *
 * For references, LLM identifies only keyworded arguments, not positional
 * arguments. Therefore, the TypeScript parameters type must be an object type,
 * and its properties must be static. If dynamic properties are, it will be
 * compilation error.
 *
 * Also, such parameters type can be utilized not only for the LLM function
 * calling, but also for the LLM structured outputs. The LLM structured outputs
 * is a feature that LLM (Large Language Model) can generate a structured
 * output, not only a plain text, by filling the parameters from the
 * conversation (maybe chatting text) with user (human).
 *
 * @template Parameters Target parameters type
 * @template Config Configuration of LLM schema composition
 * @returns LLM parameters schema
 * @reference https://platform.openai.com/docs/guides/function-calling
 * @reference https://platform.openai.com/docs/guides/structured-outputs
 */
export declare function parameters(): never;
/**
 * TypeScript parameters to LLM parameters schema.
 *
 * Creates an LLM (Large Language Model) parameters schema, a type metadata that
 * is used in the [LLM function
 * calling](https://platform.openai.com/docs/guides/function-calling) and [LLM
 * structured
 * outputs](https://platform.openai.com/docs/guides/structured-outputs), from a
 * TypeScript parameters type.
 *
 * For references, LLM identifies only keyworded arguments, not positional
 * arguments. Therefore, the TypeScript parameters type must be an object type,
 * and its properties must be static. If dynamic properties are, it will be
 * compilation error.
 *
 * Also, such parameters type can be utilized not only for the LLM function
 * calling, but also for the LLM structured outputs. The LLM structured outputs
 * is a feature that LLM (Large Language Model) can generate a structured
 * output, not only a plain text, by filling the parameters from the
 * conversation (maybe chatting text) with user (human).
 *
 * @template Parameters Target parameters type
 * @template Config Configuration of LLM schema composition
 * @returns LLM parameters schema
 * @reference https://platform.openai.com/docs/guides/function-calling
 * @reference https://platform.openai.com/docs/guides/structured-outputs
 */
export declare function parameters<Parameters extends Record<string, any>, Config extends Partial<ILlmSchema.IConfig> = {}>(): ILlmSchema.IParameters;
/**
 * > You must configure the generic argument `T`.
 *
 * TypeScript type to LLM type schema.
 *
 * Creates an LLM (Large Language Model) type schema, a type metadata that is
 * used in the [LLM function calling](@reference
 * https://platform.openai.com/docs/guides/function-calling), from a TypeScript
 * type.
 *
 * If you actually want to perform the LLM function calling with TypeScript
 * functions, you can do it with the {@link application} function. Otherwise you
 * hope to perform the structured output, {@link parameters} function is better.
 * Let's enjoy the LLM function calling and structured output with the native
 * TypeScript functions and types.
 *
 * > **What LLM function calling is?
 *
 * > LLM (Large Language Model) selects property function and fill the arguments,
 * > but actual function call execution is not by LLM, but by you.
 *
 * > In nowadays, most LLM (Large Language Model) like OpenAI are supporting
 * > "function calling" feature. The "function calling" means that LLM
 * > automatically selects a proper function and compose parameter values from the
 * > user's chatting text.
 *
 * > When LLM selects the proper function and its arguments, you just call the
 * > function with the arguments. And then informs the return value to the LLM by
 * > system prompt, LLM will continue the next conversation based on the return
 * > value.
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template T Target type
 * @template Config Configuration of LLM schema composition
 * @returns LLM schema
 * @reference https://platform.openai.com/docs/guides/function-calling
 * @reference https://platform.openai.com/docs/guides/structured-outputs
 */
export declare function schema(): never;
/**
 * TypeScript type to LLM type schema.
 *
 * Creates an LLM (Large Language Model) type schema, a type metadata that is
 * used in the [LLM function calling](@reference
 * https://platform.openai.com/docs/guides/function-calling), from a TypeScript
 * type.
 *
 * If you actually want to perform the LLM function calling with TypeScript
 * functions, you can do it with the {@link application} function. Otherwise you
 * hope to perform the structured output, {@link parameters} function is better.
 * Let's enjoy the LLM function calling and structured output with the native
 * TypeScript functions and types.
 *
 * > **What LLM function calling is?
 *
 * > LLM (Large Language Model) selects property function and fill the arguments,
 * > but actual function call execution is not by LLM, but by you.
 *
 * > In nowadays, most LLM (Large Language Model) like OpenAI are supporting
 * > "function calling" feature. The "function calling" means that LLM
 * > automatically selects a proper function and compose parameter values from the
 * > user's chatting text.
 *
 * > When LLM selects the proper function and its arguments, you just call the
 * > function with the arguments. And then informs the return value to the LLM by
 * > system prompt, LLM will continue the next conversation based on the return
 * > value.
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @template T Target type
 * @template Config Configuration of LLM schema composition
 * @returns LLM schema
 * @reference https://platform.openai.com/docs/guides/function-calling
 * @reference https://platform.openai.com/docs/guides/structured-outputs
 */
export declare function schema<T, Config extends Partial<ILlmSchema.IConfig> = {}>($defs: Record<string, ILlmSchema>): ILlmSchema;