makers-api.js

const {TextServiceClient} = require('@google-ai/generativelanguage').v1beta2;
const {GoogleAuth} = require('google-auth-library');
const {DiscussServiceClient} = require('@google-ai/generativelanguage');

const chat = async () => {
    const MODEL_NAME = 'models/chat-bison-001';
    const API_KEY = process.env.GOOGLE_PALM_API_KEY;

    const client = new DiscussServiceClient({
        authClient: new GoogleAuth().fromAPIKey(API_KEY),
    });

    const result = await client.generateMessage({
        model: MODEL_NAME,
        temperature: 0.5, // Optional. Value `0.0` always uses the highest-probability result.
        candidateCount: 1, // Optional. The number of candidate results to generate.
        prompt: {
            // optional, preamble context to prime responses
            context: 'Respond to all questions with a rhyming poem.',
            // Optional. Examples for further fine-tuning of responses.
            examples: [
                {
                    input: {content: 'What is the capital of California?'},
                    output: {
                        content:
                            `If the capital of California is what you seek,
Sacramento is where you ought to peek.`,
                    },
                },
            ],
            // Required. Alternating prompt/response messages.
            messages: [{content: 'How tall is the Eiffel Tower?'}],
        },
    });

    console.log(result[0].candidates[0].content);
    return result;
};

const prompt = async (prompt, config) => {
    const MODEL_NAME = 'models/text-bison-001';
    const API_KEY = process.env.GOOGLE_PALM_API_KEY;

    const client = new TextServiceClient({
        authClient: new GoogleAuth().fromAPIKey(API_KEY),
    });

    console.log(`Calling MakerSuite API with prompt: ${prompt}`);
    const result = await client.generateText({
        model: MODEL_NAME,
        prompt: {
            text: prompt,
        },
        ...config,
    });
    console.log('Result:', JSON.stringify(result));
    return result;
};

const embeddings = async (text) => {
    const MODEL_NAME = 'models/embedding-gecko-001';
    const API_KEY = process.env.GOOGLE_PALM_API_KEY;

    const client = new TextServiceClient({
        authClient: new GoogleAuth().fromAPIKey(API_KEY),
    });

    const result = await client.embedText({
        model: MODEL_NAME,
        text: text,
    });

    // console.log(JSON.stringify(result[0].embedding.value));
    return result[0].embedding;
};

module.exports = {
    prompt,
    embeddings,
    chat,
};