ultimate-mcp-server/dist/providers/google.js

The definitive all-in-one Model Context Protocol server for AI-assisted coding across 30+ platforms

import { GoogleGenerativeAI } from "@google/generative-ai";
import { Logger } from "../utils/logger.js";
export class GoogleProvider {
    name = "google";
    genAI;
    logger;
    models = [
        {
            id: "gemini-pro",
            name: "Gemini Pro",
            contextLength: 32000,
            pricing: { input: 0.00000125, output: 0.00000375 },
            capabilities: ["coding", "reasoning", "analysis"],
            provider: "google",
        },
        {
            id: "gemini-pro-vision",
            name: "Gemini Pro Vision",
            contextLength: 32000,
            pricing: { input: 0.00000125, output: 0.00000375 },
            capabilities: ["coding", "reasoning", "vision"],
            provider: "google",
        },
    ];
    constructor(apiKey) {
        this.logger = new Logger("GoogleProvider");
        this.genAI = new GoogleGenerativeAI(apiKey);
    }
    async complete(prompt, options) {
        try {
            const model = this.genAI.getGenerativeModel({
                model: options?.model || "gemini-pro",
            });
            const result = await model.generateContent({
                contents: [{ role: "user", parts: [{ text: prompt }] }],
                generationConfig: {
                    temperature: options?.temperature ?? 0.7,
                    maxOutputTokens: options?.maxTokens,
                    topP: options?.topP,
                },
            });
            const response = await result.response;
            return response.text();
        }
        catch (error) {
            this.logger.error("Google completion failed:", error);
            throw error;
        }
    }
    async completeWithContext(messages, options) {
        try {
            const model = this.genAI.getGenerativeModel({
                model: options?.model || "gemini-pro",
            });
            const contents = messages.map(msg => ({
                role: msg.role === 'assistant' ? 'model' : 'user',
                parts: [{ text: msg.content }],
            }));
            const result = await model.generateContent({
                contents,
                generationConfig: {
                    temperature: options?.temperature ?? 0.7,
                    maxOutputTokens: options?.maxTokens,
                    topP: options?.topP,
                },
            });
            const response = await result.response;
            return response.text();
        }
        catch (error) {
            this.logger.error("Google completion with context failed:", error);
            throw error;
        }
    }
}
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