@juspay/neurolink/dist/lib/providers/googleVertex.d.ts

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import type { ZodType, ZodTypeDef } from "zod";
import { type Schema, type LanguageModelV1, type LanguageModel } from "ai";
import type { AIProviderName } from "../core/types.js";
import type { StreamOptions, StreamResult } from "../types/streamTypes.js";
import { BaseProvider } from "../core/baseProvider.js";
/**
 * Google Vertex AI Provider v2 - BaseProvider Implementation
 *
 * Features:
 * - Extends BaseProvider for shared functionality
 * - Preserves existing Google Cloud authentication
 * - Maintains Anthropic model support via dynamic imports
 * - Fresh model creation for each request
 * - Enhanced error handling with setup guidance
 * - Tool registration and context management
 */
export declare class GoogleVertexProvider extends BaseProvider {
    private projectId;
    private location;
    private registeredTools;
    private toolContext;
    private static modelConfigCache;
    private static modelConfigCacheTime;
    private static readonly CACHE_DURATION;
    private static readonly MAX_CACHE_SIZE;
    private static maxTokensCache;
    private static maxTokensCacheTime;
    constructor(modelName?: string, providerName?: string, sdk?: unknown);
    protected getProviderName(): AIProviderName;
    protected getDefaultModel(): string;
    /**
     * Returns the Vercel AI SDK model instance for Google Vertex
     * Creates fresh model instances for each request
     */
    protected getAISDKModel(): Promise<LanguageModel>;
    /**
     * Gets the appropriate model instance (Google or Anthropic)
     * Creates fresh instances for each request to ensure proper authentication
     */
    private getModel;
    protected executeStream(options: StreamOptions, analysisSchema?: ZodType<unknown, ZodTypeDef, unknown> | Schema<unknown>): Promise<StreamResult>;
    protected handleProviderError(error: unknown): Error;
    /**
     * Memory-safe cache management for model configurations
     * Implements LRU eviction to prevent memory leaks in long-running processes
     */
    private static evictLRUCacheEntries;
    /**
     * Access and refresh cache entry (moves to end for LRU)
     */
    private static accessCacheEntry;
    /**
     * Memory-safe cached check for whether maxTokens should be set for the given model
     * Optimized for streaming performance with LRU eviction to prevent memory leaks
     */
    private shouldSetMaxTokensCached;
    /**
     * Memory-safe check if model has maxTokens issues using configuration-based approach
     * This replaces hardcoded model-specific logic with configurable behavior
     * Includes LRU caching to avoid repeated configuration lookups during streaming
     */
    private modelHasMaxTokensIssues;
    /**
     * Check if Anthropic models are available
     * @returns Promise<boolean> indicating if Anthropic support is available
     */
    hasAnthropicSupport(): Promise<boolean>;
    /**
     * Create an Anthropic model instance if available
     * Uses fresh vertex settings for each request
     * @param modelName Anthropic model name (e.g., 'claude-3-sonnet@20240229')
     * @returns LanguageModelV1 instance or null if not available
     */
    createAnthropicModel(modelName: string): Promise<LanguageModelV1 | null>;
    /**
     * Register a tool with the AI provider
     * @param name The name of the tool
     * @param schema The Zod schema defining the tool's parameters
     * @param description A description of what the tool does
     * @param handler The function to execute when the tool is called
     */
    registerTool(name: string, schema: ZodType<unknown>, description: string, handler: (params: Record<string, unknown>) => Promise<unknown>): void;
    /**
     * Set the context for tool execution
     * @param context The context to use for tool execution
     */
    setToolContext(context: Record<string, unknown>): void;
    /**
     * Get the current tool execution context
     * @returns The current tool execution context
     */
    getToolContext(): Record<string, unknown>;
    /**
     * Set the tool executor function for custom tool execution
     * This method is called by BaseProvider.setupToolExecutor()
     * @param executor Function to execute tools by name
     */
    setToolExecutor(executor: (toolName: string, params: unknown) => Promise<unknown>): void;
    /**
     * Clear all static caches - useful for testing and memory cleanup
     * Public method to allow external cache management
     */
    static clearCaches(): void;
    /**
     * Get cache statistics for monitoring and debugging
     */
    static getCacheStats(): {
        modelConfigCacheSize: number;
        maxTokensCacheSize: number;
        maxCacheSize: number;
        cacheAge: {
            modelConfig: number;
            maxTokens: number;
        };
    };
    /**
     * Get model suggestions when a model is not found
     */
    private getModelSuggestions;
}
export default GoogleVertexProvider;
export { GoogleVertexProvider as GoogleVertexAI };