Description comments on LLM module.

benchmark/package.json

@@ -72,6 +72,6 @@
     "suppress-warnings": "^1.0.2",
     "tstl": "^3.0.0",
     "uuid": "^9.0.1",
-    "typia": "../typia-6.10.0-dev.20240906.tgz"
+    "typia": "../typia-6.10.0-dev.20240907.tgz"
   }
 }

errors/package.json

@@ -32,6 +32,6 @@
     "typescript": "^5.3.2"
   },
   "dependencies": {
-    "typia": "../typia-6.10.0-dev.20240906.tgz"
+    "typia": "../typia-6.10.0-dev.20240907.tgz"
   }
 }

src/llm.ts

@@ -1,7 +1,72 @@
 import { ILlmApplication, ILlmSchema } from "@samchon/openapi";
 
-export function application(): never;
-export function application<T extends object>(): ILlmApplication;
+/**
+ * > You must configure the generic argument `App`.
+ *
+ * TypeScript functions to LLM function schemas.
+ *
+ * Creates an application of LLM (Large Language Model) function calling schemas from
+ * a TypeScript class or interface type containig 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) that must be
+ * composed by human, not by LLM. File uploading feature or some sensitive information
+ * like secrety key (password) are the examples. In that case, you can separate the
+ * function parameters to both LLM and human sides by configuring the
+ * {@link ILlmApplication.IOptions.separate} property.
+ *
+ * Additionally, 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.
+ *
+ * @template App Target class or interface type collecting the functions to call
+ * @param options Options for the LLM application construction
+ * @returns Application of LLM function calling schemas
+ * @reference https://platform.openai.com/docs/guides/function-calling
+ * @author Jeongho Nam - https://github.com/samchon
+ */
+export function application(options?: ILlmApplication.IOptions): never;
+
+/**
+ * TypeScript functions to LLM function schemas.
+ *
+ * Creates an application of LLM (Large Language Model) function calling schemas from
+ * a TypeScript class or interface type containig 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) that must be
+ * composed by human, not by LLM. File uploading feature or some sensitive information
+ * like secrety key (password) are the examples. In that case, you can separate the
+ * function parameters to both LLM and human sides by configuring the
+ * {@link ILlmApplication.IOptions.separate} property.
+ *
+ * Additionally, 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.
+ *
+ * @template App Target class or interface type collecting the functions to call
+ * @param options Options for the LLM application construction
+ * @returns Application of LLM function calling schemas
+ * @reference https://platform.openai.com/docs/guides/function-calling
+ * @author Jeongho Nam - https://github.com/samchon
+ */
+export function application<App extends object>(
+  options?: ILlmApplication.IOptions,
+): ILlmApplication;
 
 /**
  * @internal
@@ -10,7 +75,78 @@ export function application(): never {
   halt("application");
 }
 
+/**
+ * > 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.
+ *
+ * The returned {@link ILlmSchema} type is similar to the OpenAPI v3.0 based JSON schema
+ * definition, but it is more simplified for the LLM function calling by remmoving the
+ * {@link OpenApiV3.IJson.IReference reference} type embodied by the
+ * {@link OpenApiV3.IJson.IReference.$ref `$ref`} proeprty.
+ *
+ * If you actually want to perform the LLM function calling with TypeScript functions,
+ * you can do it with the {@link application} function. Let's enjoy the
+ * LLM function calling with native TypeScript functions and types.
+ *
+ * > **What LLM function calling is?
+ * >
+ * > LLM (Large Language Model) selects propert function and fill the arguments,
+ * > but actuall 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.
+ *
+ * @template T Target type
+ * @returns LLM schema
+ * @reference https://platform.openai.com/docs/guides/function-calling
+ * @author Jeongho Nam - https://github.com/samchon
+ */
 export 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.
+ *
+ * The returned {@link ILlmSchema} type is similar to the OpenAPI v3.0 based JSON schema
+ * definition, but it is more simplified for the LLM function calling by remmoving the
+ * {@link OpenApiV3.IJson.IReference reference} type embodied by the
+ * {@link OpenApiV3.IJson.IReference.$ref `$ref`} proeprty.
+ *
+ * If you actually want to perform the LLM function calling with TypeScript functions,
+ * you can do it with the {@link application} function. Let's enjoy the
+ * LLM function calling with native TypeScript functions and types.
+ *
+ * > **What LLM function calling is?
+ * >
+ * > LLM (Large Language Model) selects propert function and fill the arguments,
+ * > but actuall 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.
+ *
+ * @template T Target type
+ * @returns LLM schema
+ * @reference https://platform.openai.com/docs/guides/function-calling
+ * @author Jeongho Nam - https://github.com/samchon
+ */
 export function schema<T>(): ILlmSchema;
 
 /**

test-esm/package.json

@@ -36,6 +36,6 @@
     "typescript": "^5.4.5"
   },
   "dependencies": {
-    "typia": "../typia-6.10.0-dev.20240906.tgz"
+    "typia": "../typia-6.10.0-dev.20240907.tgz"
   }
 }