pulse-ai-utils/helpers/gemini-helper.d.ts

Utility functions and helpers for AI-powered applications

import OpenAI from 'openai';
import { LLMBase, LLMConfig } from './llm-base';
import { TweetSchema } from 'pulse-type-registry';
import { z } from 'zod';
/**
 * Gemini Helper - Direct Google Gemini API integration
 * Supports web search and structured output (in different models)
 */
export default class GeminiHelper extends LLMBase {
    private genAI;
    private webSearchModel;
    private structuredOutputModel;
    constructor(apiKey?: string, openaiInstance?: OpenAI, model?: string);
    /**
     * Create Gemini helper with model from remote config
     */
    static createWithRemoteConfig(apiKey?: string, openaiInstance?: OpenAI): Promise<GeminiHelper>;
    protected createOpenAIInstance(config: LLMConfig): OpenAI;
    protected getProviderName(): string;
    /**
     * Generate embeddings using Gemini
     * Note: Gemini doesn't have a dedicated embedding model yet
     */
    generateEmbedding(text: string): Promise<number[]>;
    /**
     * Fetch latest tweets for a given area using two-step Gemini process
     * Step 1: Web search with Gemini Flash Exp (supports web search)
     * Step 2: Structure the results with Gemini Flash (supports structured output)
     */
    fetchLatestTweets(area: string, region: string, country: string): Promise<z.infer<typeof TweetSchema>[]>;
    /**
     * Fetch local content (events, deals, news, reels, places) using two-step Gemini process
     * Similar to runQuery and fetchStructuredDataFromWeb but using Gemini's web search
     */
    fetchLocalContent(area: string, region: string, country: string, categories?: string[]): Promise<any[]>;
    /**
     * Override the base class method to use Gemini's native API
     */
    query(prompt: string, outputFormat?: any, model?: string, _systemPrompt?: string): Promise<any>;
}