@generationapi/genapi NPM

GenAPI - the fastest and best way to build AI-backed APIs

Introduction

This project provides a framework for quickly and easily building AI-backed API endpoints with LLMs. By leveraging the OpenAPI 3.1.0 specification, the system ensures accurate and context-aware responses, making it an ideal solution for testing and rapid prototyping.

Installation

Prerequisites

Node.js (version 16.x or higher recommended)
npm (bundled with Node.js) or Yarn

Steps

Clone the repository:

git clone https://github.com/generationapi/genapi

Navigate to the project directory:
```
cd genapi
```

Install dependencies:

npm install
# or, if you're using Yarn:
yarn install

Usage

Basic Usage

To start processing requests, you need to initialize the GenApi class with your OpenAPI specification, model name, and API key. Here's a simple example:

import GenApi from '@generationapi/genapi';

const openApiSpec = {
  "openapi": "3.1.0",
  "info": {
    "title": "Hello World",
    "version": "1.0.0",
    "description": "Respond with Hello to the name input in a language other than English. Always use a random language."
  },
  "paths": {
    "/hello-world": {
      "post": {
        "tags": [],
        "summary": "Hello World",
        "description": "Respond with Hello to the name input in a language other than English. Always use a random language.",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "text": {
                    "type": "string",
                    "title": "User Name",
                    "description": "The name of the user to be greeted."
                  }
                }
              },
              "examples": {
                "Request": {
                  "value": {
                    "text": "John"
                  },
                  "summary": "Simple text submission"
                }
              }
            }
          },
          "required": true
        },
        "x-prompt": "Given a text input, respond with &#x27;Hello&#x27; followed by the name in a random non-English language. Ensure the output is in the format specified by the &#x60;response_schema&#x60;.",
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "properties": {
                    "text": {
                      "type": "string"
                    }
                  }
                }
              }
            },
            "description": "Successfully processed the request, returning a structured response from the model."
          },
          "400": {
            "description": "Invalid input, e.g., missing or invalid `modelId` or data."
          },
          "401": {
            "description": "Authentication information is missing or invalid."
          },
          "404": {
            "description": "The specified `modelId` was not found."
          }
        }
      }
    }
  }
};

const modelName = 'model-id'; // "google-gemini, gpt-4, gpt-3.5-turbo, mistral-large-latest, claude-3-sonnet"
const apiKey = 'your-api-key';

const apiProcessor = new GenApi(openApiSpec, modelName, apiKey);

// Example request
const pathName = '/your/api/path';
const method = 'post';
const requestData = {
  "text": "Paul"
};

apiProcessor.processRequest(pathName, method, requestData)
  .then(response => console.log(response))
  .catch(error => console.error(error));

Advanced Configuration

You can customize the request processing by using the beforePromptGeneration and afterResponseParsing hooks:

apiProcessor.beforePromptGeneration((data) => {
  // Modify the request data before generating the prompt
  return modifiedData;
});

apiProcessor.afterResponseParsing((response) => {
  // Process the response data after parsing
  return modifiedResponse;
});

Configuration

The system is highly configurable to suit various use cases. Key configuration options include:

Model Name: Choose between different language models like GPT or Gemini based on your needs.
API Key: Securely provide your API key for model access.
Retry Strategy: Customize the retry logic for handling request failures.