The Gemini API, developed by Google, offers advanced capabilities for integrating natural language processing and machine learning into applications. It is particularly useful when combined with an OpenAI fallback, which can enhance the robustness and flexibility of your project. Here’s a guide on how to use the Gemini API with an OpenAI fallback in TypeScript. First, you need to set up your environment. Ensure you have Node.js installed on your machine, and then create a new directory for your project. Navigate to this directory in your terminal and initialize a new Node.js project by running `npm init -y`. Next, install the necessary dependencies by executing `npm install @google-ai/generativelanguage node-fetch openai`. Once your environment is ready, you can start by importing the required modules in your TypeScript file: ```typescript import { TextGenerationServiceClient } from '@google-ai/generativelanguage'; import fetch from 'node-fetch'; import { Configuration, OpenAIApi } from 'openai'; ``` You will need API keys for both Gemini and OpenAI. Store these keys securely, such as in environment variables or a configuration file. For simplicity, let's assume you are using environment variables: ```typescript const GEMINI_API_KEY = process.env.GEMINI_API_KEY; const OPENAI_API_KEY = process.env.OPENAI_API_KEY; ``` Next, initialize the clients for both APIs: ```typescript const configuration = new Configuration({ apiKey: OPENAI_API_KEY, }); const openai = new OpenAIApi(configuration); const textServiceClient = new TextGenerationServiceClient({ credentials: { client_email: '[email protected]', private_key: 'your-private-key' }, }); ``` Now, you can create a function to handle text generation. This function will first attempt to use the Gemini API and fall back to OpenAI if the Gemsini API fails: ```typescript async function generateText(prompt: string): Promise<string> { try { // Use the Gemini API const [response] = await textServiceClient.generateText({ model: 'google/api-name', // Replace with your model prompt: prompt, }); return response.candidates[0].outputText; } catch (error) { console.error('Gemini API request failed:', error); try { // Fall back to OpenAI const response = await openai.createCompletion({ model: 'text-davinci-003', // Replace with your model prompt: prompt, max_tokens: 200, }); return response.data.choices[0].text; } catch (error) { console.error('OpenAI request failed:', error); throw new Error('Both API requests failed.'); } } } ``` This function takes a `prompt` as input and attempts to generate a response using the Gemini API. If the Gemini API request fails, it logs the error and tries to generate a response using the OpenAI API. If both requests fail, it throws an error. To use this function, you can create a simple script that calls it with a specific prompt: ```typescript (async () => { const prompt = 'What is the capital of France?'; try { const result = await generateText(prompt); console.log('Generated text:', result); } catch (error) { console.error('Failed to generate text:', error); } })(); ``` This script sets up a prompt, calls the `generateText` function, and logs the result. If the function encounters an error, it catches the exception and logs an appropriate message. By combining the Gemini API with an OpenAI fallback, you can ensure that your application can handle failures and provide reliable text generation. This approach leverages the strengths of both APIs, offering a resilient solution for integrating language processing into your projects.