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OpenAPI definitions, converters and LLM function calling schema composer.

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The `@samchon/openapi` repository is a collection of OpenAPI types and converters for various versions of OpenAPI specifications. It includes an 'emended' OpenAPI v3.1 specification that enhances clarity by removing ambiguous and duplicated expressions. The repository also provides an application composer for LLM (Large Language Model) function calling from OpenAPI documents, allowing users to easily perform LLM function calls based on the Swagger document. Conversions to different versions of OpenAPI documents are also supported, all based on the emended OpenAPI v3.1 specification. Users can validate their OpenAPI documents using the `typia` library with `@samchon/openapi` types, ensuring compliance with standard specifications.

README:

@samchon/openapi

flowchart
  subgraph "OpenAPI Specification"
    v20("Swagger v2.0") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
    v30("OpenAPI v3.0") --upgrades--> emended
    v31("OpenAPI v3.1") --emends--> emended
  end
  subgraph "OpenAPI Generator"
    emended --normalizes--> migration[["Migration Schema"]]
    migration --"Artificial Intelligence"--> lfc{{"LLM Function Calling"}}
    lfc --"OpenAI"--> chatgpt("ChatGPT")
    lfc --"Anthropic"--> claude("Claude")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Meta"--> llama("Llama")
  end

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OpenAPI definitions, converters and LLM function calling application composer.

@samchon/openapi is a collection of OpenAPI types for every versions, and converters for them. In the OpenAPI types, there is an "emended" OpenAPI v3.1 specification, which has removed ambiguous and duplicated expressions for the clarity. Every conversions are based on the emended OpenAPI v3.1 specification.

  1. Swagger v2.0
  2. OpenAPI v3.0
  3. OpenAPI v3.1
  4. OpenAPI v3.1 emended

@samchon/openapi also provides LLM (Large Language Model) function calling application composer from the OpenAPI document with many strategies. With the HttpLlm module, you can perform the LLM funtion calling extremely easily just by delivering the OpenAPI (Swagger) document.

Setup

npm install @samchon/openapi

Just install by npm i @samchon/openapi command.

Here is an example code utilizing the @samchon/openapi for LLM function calling purpose.

import {
  HttpLlm,
  IChatGptSchema,
  IHttpLlmApplication,
  IHttpLlmFunction,
  OpenApi,
  OpenApiV3,
  OpenApiV3_1,
  SwaggerV2,
} from "@samchon/openapi";
import fs from "fs";
import typia from "typia";

const main = async (): Promise<void> => {
  // read swagger document and validate it
  const swagger:
    | SwaggerV2.IDocument
    | OpenApiV3.IDocument
    | OpenApiV3_1.IDocument = JSON.parse(
    await fs.promises.readFile("swagger.json", "utf8"),
  );
  typia.assert(swagger); // recommended

  // convert to emended OpenAPI document,
  // and compose LLM function calling application
  const document: OpenApi.IDocument = OpenApi.convert(swagger);
  const application: IHttpLlmApplication<"chatgpt"> = HttpLlm.application({
    model: "chatgpt",
    document,
  });

  // Let's imagine that LLM has selected a function to call
  const func: IHttpLlmFunction<"chatgpt"> | undefined = 
    application.functions.find(
      // (f) => f.name === "llm_selected_fuction_name"
      (f) => f.path === "/bbs/articles" && f.method === "post",
    );
  if (func === undefined) throw new Error("No matched function exists.");

  // actual execution is by yourself
  const article = await HttpLlm.execute({
    connection: {
      host: "http://localhost:3000",
    },
    application,
    function: func,
    arguments: {
      // arguments composed by LLM 
      body: {
        title: "Hello, world!",
        body: "Let's imagine that this argument is composed by LLM.",
        thumbnail: null,
      },
    },
  });
  console.log("article", article);
};
main().catch(console.error);

OpenAPI Definitions

flowchart
  v20(Swagger v2.0) --upgrades--> emended[["<b><u>OpenAPI v3.1 (emended)</u></b>"]]
  v30(OpenAPI v3.0) --upgrades--> emended
  v31(OpenAPI v3.1) --emends--> emended
  emended --downgrades--> v20d(Swagger v2.0)
  emended --downgrades--> v30d(Swagger v3.0)

@samchon/openapi support every versions of OpenAPI specifications with detailed TypeScript types.

Also, @samchon/openapi provides "emended OpenAPI v3.1 definition" which has removed ambiguous and duplicated expressions for clarity. It has emended original OpenAPI v3.1 specification like above. You can compose the "emended OpenAPI v3.1 document" by calling the OpenApi.convert() function.

  • Operation
    • Merge OpenApiV3_1.IPathItem.parameters to OpenApi.IOperation.parameters
    • Resolve references of OpenApiV3_1.IOperation members
    • Escape references of OpenApiV3_1.IComponents.examples
  • JSON Schema
    • Decompose mixed type: OpenApiV3_1.IJsonSchema.IMixed
    • Resolve nullable property: OpenApiV3_1.IJsonSchema.__ISignificant.nullable
    • Array type utilizes only single OpenAPI.IJsonSchema.IArray.items
    • Tuple type utilizes only OpenApi.IJsonSchema.ITuple.prefixItems
    • Merge OpenApiV3_1.IJsonSchema.IAnyOf to OpenApi.IJsonSchema.IOneOf
    • Merge OpenApiV3_1.IJsonSchema.IRecursiveReference to OpenApi.IJsonSchema.IReference
    • Merge OpenApiV3_1.IJsonSchema.IAllOf to OpenApi.IJsonSchema.IObject

Conversions to another version's OpenAPI document is also based on the "emended OpenAPI v3.1 specification" like above diagram. You can do it through OpenApi.downgrade() function. Therefore, if you want to convert Swagger v2.0 document to OpenAPI v3.0 document, you have to call two functions; OpenApi.convert() and then OpenApi.downgrade().

At last, if you utilize typia library with @samchon/openapi types, you can validate whether your OpenAPI document is following the standard specification or not. Just visit one of below playground links, and paste your OpenAPI document URL address. This validation strategy would be superior than any other OpenAPI validator libraries.

import { OpenApi, OpenApiV3, OpenApiV3_1, SwaggerV2 } from "@samchon/openapi";
import typia from "typia";
 
const main = async (): Promise<void> => {
  // GET YOUR OPENAPI DOCUMENT
  const response: Response = await fetch(
    "https://raw.githubusercontent.com/samchon/openapi/master/examples/v3.0/openai.json"
  );
  const document: any = await response.json();
 
  // TYPE VALIDATION
  const result = typia.validate<
    | OpenApiV3_1.IDocument
    | OpenApiV3.IDocument
    | SwaggerV2.IDocument
  >(document);
  if (result.success === false) {
    console.error(result.errors);
    return;
  }
 
  // CONVERT TO EMENDED
  const emended: OpenApi.IDocument = OpenApi.convert(document);
  console.info(emended);
};
main().catch(console.error);

LLM Function Calling

Preface

flowchart TD
  subgraph "OpenAPI Specification"
    v20("Swagger v2.0") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
    v30("OpenAPI v3.0") --upgrades--> emended
    v31("OpenAPI v3.1") --emends--> emended
  end
  subgraph "OpenAPI Generator"
    emended --normalizes--> migration[["Migration Schema"]]
    migration --"Artificial Intelligence"--> lfc{{"<b><u>LLM Function Calling</b></u>"}}
    lfc --"OpenAI"--> chatgpt("ChatGPT")
    lfc --"Anthropic"--> claude("Claude")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Meta"--> llama("Llama")
  end

LLM function calling application from OpenAPI document.

@samchon/openapi provides LLM (Large Language Model) funtion calling application from the "emended OpenAPI v3.1 document". Therefore, if you have any HTTP backend server and succeeded to build an OpenAPI document, you can easily make the A.I. chatbot application.

In the A.I. chatbot, LLM will select proper function to remotely call from the conversations with user, and fill arguments of the function automatically. If you actually execute the function call through the HttpLlm.execute() funtion, it is the "LLM function call."

Let's enjoy the fantastic LLM function calling feature very easily with @samchon/openapi.

[!NOTE]

You also can compose ILlmApplication from a class type with typia.

https://typia.io/docs/llm/application

import { ILlmApplication } from "@samchon/openapi";
import typia from "typia";

const app: ILlmApplication<"chatgpt"> =
  typia.llm.application<YourClassType, "chatgpt">();

[!TIP]

LLM selects proper function and fill arguments.

In nowadays, most LLM (Large Language Model) like OpenAI are supporting "function calling" feature. The "LLM function calling" means that LLM automatically selects a proper function and fills parameter values from conversation with the user (may by chatting text).

https://platform.openai.com/docs/guides/function-calling

Execution

Actual function call execution is by yourself.

LLM (Large Language Model) providers like OpenAI selects a proper function to call from the conversations with users, and fill arguments of it. However, function calling feature supported by LLM providers do not perform the function call execution. The actual execution responsibility is on you.

In @samchon/openapi, you can execute the LLM function calling by HttpLlm.execute() (or HttpLlm.propagate()) function. Here is an example code executing the LLM function calling through the HttpLlm.execute() function. As you can see, to execute the LLM function call, you have to deliver these informations:

  • Connection info to the HTTP server
  • Application of the LLM fuction calling
  • LLM function schema to call
  • Arguments for the function call (maybe composed by LLM)

Here is the example code executing the LLM function call with @samchon/openapi.

import {
  HttpLlm,
  IChatGptSchema,
  IHttpLlmApplication,
  IHttpLlmFunction,
  OpenApi,
  OpenApiV3,
  OpenApiV3_1,
  SwaggerV2,
} from "@samchon/openapi";
import OpenAI from "openai";
import typia from "typia";

const main = async (): Promise<void> => {
  // Read swagger document and validate it
  const swagger:
    | SwaggerV2.IDocument
    | OpenApiV3.IDocument
    | OpenApiV3_1.IDocument = JSON.parse(
    await fetch(
      "https://github.com/samchon/shopping-backend/blob/master/packages/api/swagger.json",
    ).then((r) => r.json()),
  );
  typia.assert(swagger); // recommended

  // convert to emended OpenAPI document,
  // and compose LLM function calling application
  const document: OpenApi.IDocument = OpenApi.convert(swagger);
  const application: IHttpLlmApplication<"chatgpt"> = HttpLlm.application({
    model: "chatgpt",
    document,
  });

  // Let's imagine that LLM has selected a function to call
  const func: IHttpLlmFunction<"chatgpt"> | undefined =
    application.functions.find(
      // (f) => f.name === "llm_selected_fuction_name"
      (f) => f.path === "/shoppings/sellers/sale" && f.method === "post",
    );
  if (func === undefined) throw new Error("No matched function exists.");

  // Get arguments by ChatGPT function calling
  const client: OpenAI = new OpenAI({
    apiKey: "<YOUR_OPENAI_API_KEY>",
  });
  const completion: OpenAI.ChatCompletion =
    await client.chat.completions.create({
      model: "gpt-4o",
      messages: [
        {
          role: "assistant",
          content:
            "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
        },
        {
          role: "user",
          content: "<DESCRIPTION ABOUT THE SALE>",
          // https://github.com/samchon/openapi/blob/master/examples/function-calling/prompts/microsoft-surface-pro-9.md
        },
      ],
      tools: [
        {
          type: "function",
          function: {
            name: func.name,
            description: func.description,
            parameters: func.parameters as Record<string, any>,
          },
        },
      ],
    });
  const toolCall: OpenAI.ChatCompletionMessageToolCall =
    completion.choices[0].message.tool_calls![0];

  // Actual execution by yourself
  const article = await HttpLlm.execute({
    connection: {
      host: "http://localhost:37001",
    },
    application,
    function: func,
    input: JSON.parse(toolCall.function.arguments),
  });
  console.log("article", article);
};
main().catch(console.error);

Separation

Arguments from both Human and LLM sides.

When composing parameter arguments through the LLM (Large Language Model) function calling, there can be a case that some parameters (or nested properties) must be composed not by LLM, but by Human. File uploading feature, or sensitive information like secret key (password) cases are the representative examples.

In that case, you can configure the LLM function calling schemas to exclude such Human side parameters (or nested properties) by IHttpLlmApplication.options.separate property. Instead, you have to merge both Human and LLM composed parameters into one by calling the HttpLlm.mergeParameters() before the LLM function call execution of HttpLlm.execute() function.

Here is the example code separating the file uploading feature from the LLM function calling schema, and combining both Human and LLM composed parameters into one before the LLM function call execution.

import Anthropic from "@anthropic-ai/sdk";
import {
  ClaudeTypeChecker,
  HttpLlm,
  IClaudeSchema,
  IHttpLlmApplication,
  IHttpLlmFunction,
  OpenApi,
  OpenApiV3,
  OpenApiV3_1,
  SwaggerV2,
} from "@samchon/openapi";
import typia from "typia";

const main = async (): Promise<void> => {
  // Read swagger document and validate it
  const swagger:
    | SwaggerV2.IDocument
    | OpenApiV3.IDocument
    | OpenApiV3_1.IDocument = JSON.parse(
    await fetch(
      "https://github.com/samchon/shopping-backend/blob/master/packages/api/swagger.json",
    ).then((r) => r.json()),
  );
  typia.assert(swagger); // recommended

  // convert to emended OpenAPI document,
  // and compose LLM function calling application
  const document: OpenApi.IDocument = OpenApi.convert(swagger);
  const application: IHttpLlmApplication<"claude"> = HttpLlm.application({
    model: "claude",
    document,
    options: {
      reference: true,
      separate: (schema) =>
        ClaudeTypeChecker.isString(schema) &&
        !!schema.contentMediaType?.startsWith("image"),
    },
  });

  // Let's imagine that LLM has selected a function to call
  const func: IHttpLlmFunction<"claude"> | undefined =
    application.functions.find(
      // (f) => f.name === "llm_selected_fuction_name"
      (f) => f.path === "/shoppings/sellers/sale" && f.method === "post",
    );
  if (func === undefined) throw new Error("No matched function exists.");

  // Get arguments by ChatGPT function calling
  const client: Anthropic = new Anthropic({
    apiKey: "<YOUR_ANTHROPIC_API_KEY>",
  });
  const completion: Anthropic.Message = await client.messages.create({
    model: "claude-3-5-sonnet-latest",
    max_tokens: 8_192,
    messages: [
      {
        role: "assistant",
        content:
          "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
      },
      {
        role: "user",
        content: "<DESCRIPTION ABOUT THE SALE>",
        // https://github.com/samchon/openapi/blob/master/examples/function-calling/prompts/microsoft-surface-pro-9.md
      },
    ],
    tools: [
      {
        name: func.name,
        description: func.description,
        input_schema: func.separated!.llm as any,
      },
    ],
  });
  const toolCall: Anthropic.ToolUseBlock = completion.content.filter(
    (c) => c.type === "tool_use",
  )[0]!;

  // Actual execution by yourself
  const article = await HttpLlm.execute({
    connection: {
      host: "http://localhost:37001",
    },
    application,
    function: func,
    input: HttpLlm.mergeParameters({
      function: func,
      llm: toolCall.input as any,
      human: {
        // Human composed parameter values
        content: {
          files: [],
          thumbnails: [
            {
              name: "thumbnail",
              extension: "jpeg",
              url: "https://serpapi.com/searches/673d3a37e45f3316ecd8ab3e/images/1be25e6e2b1fb7509f1af89c326cb41749301b94375eb5680b9bddcdf88fabcb.jpeg",
            },
            // ...
          ],
        },
      },
    }),
  });
  console.log("article", article);
};
main().catch(console.error);

For Tasks:

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tappas Screenshot
tappas

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

github
: 78
cloudflare-rag Screenshot
cloudflare-rag

This repository provides a fullstack example of building a Retrieval Augmented Generation (RAG) app with Cloudflare. It utilizes Cloudflare Workers, Pages, D1, KV, R2, AI Gateway, and Workers AI. The app features streaming interactions to the UI, hybrid RAG with Full-Text Search and Vector Search, switchable providers using AI Gateway, per-IP rate limiting with Cloudflare's KV, OCR within Cloudflare Worker, and Smart Placement for workload optimization. The development setup requires Node, pnpm, and wrangler CLI, along with setting up necessary primitives and API keys. Deployment involves setting up secrets and deploying the app to Cloudflare Pages. The project implements a Hybrid Search RAG approach combining Full Text Search against D1 and Hybrid Search with embeddings against Vectorize to enhance context for the LLM.

github
: 93
pixeltable Screenshot
pixeltable

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

github
: 122
wave-apps Screenshot
wave-apps

Wave Apps is a directory of sample applications built on H2O Wave, allowing users to build AI apps faster. The apps cover various use cases such as explainable hotel ratings, human-in-the-loop credit risk assessment, mitigating churn risk, online shopping recommendations, and sales forecasting EDA. Users can download, modify, and integrate these sample apps into their own projects to learn about app development and AI model deployment.

github
: 145