@juspay/neurolink/dist/providers/googleAiStudio/client.d.ts

The pipe layer of an AI nervous system: one interface connecting provider neurons to your application, across three inference types — generate, stream and decide. `decide` returns typed, calibrated judgements from a non-generative model (~400ms, ~$0.00002

import { type AIProviderName } from "../../constants/enums.js";
import { BaseProvider } from "../../core/baseProvider.js";
import type { EmbedInput, ZodUnknownSchema, EnhancedGenerateResult, TextGenerationOptions, StreamOptions, StreamResult } from "../../types/index.js";
import type { LanguageModel, Schema } from "../../types/index.js";
export declare class GoogleAIStudioProvider extends BaseProvider {
    private credentials?;
    constructor(modelName?: string, sdk?: unknown, credentials?: {
        apiKey?: string;
        baseURL?: string;
    });
    getProviderName(): AIProviderName;
    getDefaultModel(): string;
    /**
     * AI SDK model instance — no longer used.
     * All models are routed through native @google/genai SDK directly.
     */
    getAISDKModel(): LanguageModel;
    protected formatProviderError(error: unknown): Error;
    /**
     * Overrides the BaseProvider's image generation method to implement it for Google AI.
     * This method calls the Google AI API to generate an image from a prompt.
     * @param options The generation options containing the prompt.
     * @returns A promise that resolves to the generation result, including the image data.
     */
    protected executeImageGeneration(options: TextGenerationOptions): Promise<EnhancedGenerateResult>;
    /**
     * Detect image MIME type from buffer
     */
    private detectImageType;
    /**
     * Estimate token count from text using centralized estimation with provider multipliers
     */
    private estimateTokenCount;
    /**
     * Run the file preprocessing this provider's native paths depend on.
     *
     * AI Studio overrides both `generate()` and `executeStream()` and routes
     * straight to the native SDK, so neither reaches
     * `buildMultimodalMessagesArray` — the place that turns `input.files` into
     * text, images, PDFs and `nativeAudioFiles`. `BaseProvider.stream()` does
     * build messages, but onto a throwaway clone whose result is discarded, so
     * the real `options.input` came through untouched.
     *
     * The consequence was asymmetric and easy to miss: `generate()` did this
     * inline and worked, while `stream()` silently dropped every attached file —
     * not just audio, but the metadata summary too. Vertex hit the identical bug
     * (#1258) and solved it with exactly this shape, called from both entry
     * points.
     */
    private preprocessNativeFileInput;
    protected executeStream(options: StreamOptions, analysisSchema?: ZodUnknownSchema | Schema<unknown>): Promise<StreamResult>;
    /**
     * Execute stream using native @google/genai SDK
     * Uses @google/genai directly for all Gemini models (2.0, 2.5, 3.x)
     */
    private executeNativeGemini3Stream;
    /**
     * Execute generate using native @google/genai SDK for Gemini 3 models
     * This bypasses @ai-sdk/google to properly handle thought_signature
     */
    private executeNativeGemini3Generate;
    /**
     * Override generate to route Gemini 3 models with tools to native SDK
     */
    generate(optionsOrPrompt: TextGenerationOptions | string): Promise<EnhancedGenerateResult | null>;
    /**
     * Emit `generation:end` so the Pipeline B observability listener creates
     * a `model.generation` span for native Google AI Studio generate calls.
     * Without this hand-off the native path silently disappears from
     * Pipeline B exporters (Langfuse, custom OTEL collectors).
     */
    private emitPipelineBGenerationEvent;
    private executeAudioStreamViaGeminiLive;
    protected getDefaultEmbeddingModel(): string;
    /**
     * Generate embeddings for text using Google AI Studio embedding models
     * @param input - The text to embed (string or EmbedInput)
     * @param modelName - The embedding model to use (default: gemini-embedding-001)
     * @returns Promise resolving to the embedding vector
     */
    embed(input: string | EmbedInput, modelName?: string): Promise<number[]>;
    /**
     * Generate embeddings for multiple texts in a single batch
     * @param texts - The texts to embed
     * @param modelName - The embedding model to use (default: gemini-embedding-001)
     * @returns Promise resolving to an array of embedding vectors
     */
    embedMany(texts: string[], modelName?: string): Promise<number[][]>;
    private getApiKey;
    private getBaseURL;
}
export default GoogleAIStudioProvider;