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Automatically switches to fallback on primary provider failure.\n *\n * @category Factory\n *\n * @param primaryProvider - Primary AI provider name (default: 'bedrock')\n * @param fallbackProvider - Fallback AI provider name (default: 'vertex')\n * @param modelName - Optional model name for both providers\n * @returns Promise resolving to object with primary and fallback providers\n *\n * @example Production failover setup\n * ```typescript\n * import { createAIProviderWithFallback } from '@juspay/neurolink';\n *\n * const { primary, fallback } = await createAIProviderWithFallback('bedrock', 'vertex');\n *\n * try {\n *   const result = await primary.generate({ input: { text: 'Hello!' } });\n * } catch (error) {\n *   // Automatically use fallback\n *   const result = await fallback.generate({ input: { text: 'Hello!' } });\n * }\n * ```\n *\n * @example Multi-region setup\n * ```typescript\n * const { primary, fallback } = await createAIProviderWithFallback(\n *   'vertex',      // Primary: US region\n *   'bedrock',     // Fallback: Global\n *   'claude-3-sonnet'\n * );\n * ```\n *\n * @see {@link AIProviderFactory.createProviderWithFallback}\n * @since 1.0.0\n */\nexport async function createAIProviderWithFallback(primaryProvider, fallbackProvider, modelName) {\n    return await AIProviderFactory.createProviderWithFallback(primaryProvider || \"bedrock\", fallbackProvider || \"vertex\", modelName);\n}\n/**\n * Create the best available provider based on environment configuration.\n *\n * Intelligently selects the best provider based on available API keys\n * in environment variables. Automatically detects and configures the\n * optimal provider without manual configuration.\n *\n * @category Factory\n *\n * @param requestedProvider - Optional preferred provider name\n * @param modelName - Optional model name\n * @returns Promise resolving to the best configured provider\n *\n * @example Automatic provider selection\n * ```typescript\n * import { createBestAIProvider } from '@juspay/neurolink';\n *\n * // Automatically uses provider with configured API key\n * const provider = await createBestAIProvider();\n * const result = await provider.generate({ input: { text: 'Hello!' } });\n * ```\n *\n * @example With provider preference\n * ```typescript\n * // Tries to use OpenAI, falls back to available provider\n * const provider = await createBestAIProvider('openai');\n * ```\n *\n * @remarks\n * Environment variables checked (in order):\n * - OPENAI_API_KEY\n * - ANTHROPIC_API_KEY\n * - GOOGLE_API_KEY\n * - VERTEX_PROJECT_ID + credentials\n * - AWS credentials for Bedrock\n * - And more...\n *\n * @see {@link AIProviderFactory.createBestProvider}\n * @see {@link getBestProvider} for provider detection utility\n * @since 1.0.0\n */\nexport async function createBestAIProvider(requestedProvider, modelName) {\n    return await AIProviderFactory.createBestProvider(requestedProvider, modelName);\n}\n// ============================================================================\n// MCP PLUGIN ECOSYSTEM - Universal AI Development Platform\n// ============================================================================\n/**\n * MCP (Model Context Protocol) Plugin Ecosystem\n *\n * Extensible plugin architecture based on research blueprint for\n * transforming NeuroLink into a Universal AI Development Platform.\n *\n * @example\n * ```typescript\n * import { mcpEcosystem, readFile, writeFile } from '@juspay/neurolink';\n *\n * // Initialize the ecosystem\n * await mcpEcosystem.initialize();\n *\n * // List available plugins\n * const plugins = await mcpEcosystem.list();\n *\n * // Use filesystem operations\n * const content = await readFile('README.md');\n * await writeFile('output.txt', 'Hello from MCP!');\n * ```\n */\nexport { CircuitBreakerManager, calculateExpiresAt, \n// MCP Server Factory\ncreateMCPServer, createOAuthProviderFromConfig, DEFAULT_HTTP_RETRY_CONFIG, DEFAULT_RATE_LIMIT_CONFIG, executeMCP, FileTokenStorage, getMCPStats, getServerInfo, globalCircuitBreakerManager, globalRateLimiterManager, \n// HTTP Transport utilities\nHTTPRateLimiter, \n// OAuth Authentication\nInMemoryTokenStorage, \n// Core MCP ecosystem\n// Simplified MCP exports\ninitializeMCPEcosystem, isRetryableHTTPError, isRetryableStatusCode, isTokenExpired, listMCPs, \n// Circuit Breaker\nMCPCircuitBreaker, mcpLogger, NeuroLinkOAuthProvider, RateLimiterManager, validateServerTools, validateTool as validateMCPTool, withHTTPRetry, } from \"./mcp/index.js\";\nexport { logger } from \"./utils/logger.js\";\nexport { getPoolStats } from \"./utils/redis.js\";\n// ============================================================================\n// REAL-TIME SERVICES & TELEMETRY - Enterprise Platform Features\n// ============================================================================\n// Real-time Services (Phase 1) - Basic SSE functionality only\n// export { createEnhancedChatService } from './chat/index.js';\n// export type * from './services/types.js';\n// Optional Telemetry (Phase 2) - Telemetry service initialization\nexport async function initializeTelemetry() {\n    try {\n        const result = await init();\n        return !!result;\n    }\n    catch {\n        return false;\n    }\n}\nexport async function getTelemetryStatus() {\n    return getStatus();\n}\n/**\n * Legacy generateText function for backward compatibility.\n *\n * Provides standalone text generation function for existing code.\n * For new code, use {@link NeuroLink.generate} instead which provides\n * more features including streaming, tools, and structured output.\n *\n * @category Legacy\n * @deprecated Use {@link NeuroLink.generate} for new code\n *\n * @param options - Text generation options\n * @param options.prompt - Input prompt text\n * @param options.provider - AI provider name (e.g., 'bedrock', 'openai')\n * @param options.model - Model name to use\n * @param options.temperature - Sampling temperature (0-2)\n * @param options.maxTokens - Maximum tokens to generate\n * @returns Promise resolving to text generation result with content and metadata\n *\n * @example Basic text generation\n * ```typescript\n * import { generateText } from '@juspay/neurolink';\n *\n * const result = await generateText({\n *   prompt: 'Explain quantum computing in simple terms',\n *   provider: 'bedrock',\n *   model: 'claude-3-sonnet'\n * });\n * console.log(result.content);\n * ```\n *\n * @example With temperature control\n * ```typescript\n * const result = await generateText({\n *   prompt: 'Write a creative story',\n *   provider: 'openai',\n *   temperature: 1.5,\n *   maxTokens: 500\n * });\n * ```\n *\n * @see {@link NeuroLink.generate} for modern API with more features\n * @since 1.0.0\n */\nexport async function generateText(options) {\n    // Create instance on-demand without auto-instantiation\n    const neurolink = new NeuroLink();\n    return await neurolink.generateText(options);\n}\n// Workflow execution\nexport { runWorkflow } from \"./workflow/core/workflowRunner.js\";\n// Workflow registry\nexport { registerWorkflow, getWorkflow, listWorkflows, clearRegistry as clearWorkflowRegistry, } from \"./workflow/core/workflowRegistry.js\";\n// Pre-built workflows - Consensus\nexport { CONSENSUS_3_WORKFLOW, CONSENSUS_3_FAST_WORKFLOW, createConsensus3WithPrompt, } from \"./workflow/workflows/consensusWorkflow.js\";\n// Pre-built workflows - Fallback\nexport { FAST_FALLBACK_WORKFLOW, AGGRESSIVE_FALLBACK_WORKFLOW, } from \"./workflow/workflows/fallbackWorkflow.js\";\n// Pre-built workflows - Multi-judge\nexport { MULTI_JUDGE_5_WORKFLOW, MULTI_JUDGE_3_WORKFLOW, createMultiJudgeWorkflow, } from \"./workflow/workflows/multiJudgeWorkflow.js\";\n// Pre-built workflows - Adaptive\nexport { QUALITY_MAX_WORKFLOW, SPEED_FIRST_WORKFLOW, BALANCED_ADAPTIVE_WORKFLOW, createAdaptiveWorkflow, } from \"./workflow/workflows/adaptiveWorkflow.js\";\n// Validation and metrics\nexport { validateWorkflow } from \"./workflow/utils/workflowValidation.js\";\nexport { calculateModelMetrics, compareWorkflows, generateSummaryStats, } from \"./workflow/utils/workflowMetrics.js\";\n// Workflow constants\nexport { WORKFLOW_ENGINE_VERSION, DEFAULT_SCORE_SCALE, } from \"./workflow/index.js\";\n/**\n * Server Adapters for exposing NeuroLink as HTTP APIs\n *\n * Supports multiple frameworks: Hono, Express, Fastify, Koa\n *\n * @example\n * ```typescript\n * import { NeuroLink } from '@juspay/neurolink';\n * import { createServer } from '@juspay/neurolink/server';\n *\n * const neurolink = new NeuroLink({ provider: 'openai' });\n * const server = await createServer(neurolink, {\n *   framework: 'hono',\n *   config: { port: 3000 }\n * });\n * await server.start();\n * ```\n */\nexport { \n// Validation\nAgentExecuteRequestSchema, AlreadyRunningError, AuthenticationError, AuthorizationError, \n// Framework Adapters\nBaseServerAdapter, ConfigurationError, \n// Routes\ncreateAgentRoutes, createAgentWebSocketHandler, createAllRoutes, \n// Middleware\ncreateAuthMiddleware, createCacheInvalidator, createCacheMiddleware, createCompressionMiddleware, createDataStreamResponse, \n// Streaming\ncreateDataStreamWriter, createErrorHandlingMiddleware, createErrorResponse, createFieldValidator, createHealthRoutes, createLoggingMiddleware, createMCPRoutes, createMemoryRoutes, createNDJSONHeaders, createOpenAPIGenerator, createRateLimitMiddleware, createRequestIdMiddleware, createRequestValidationMiddleware, createRoleMiddleware, createSecurityHeadersMiddleware, \n// Server Factory\ncreateServer, createSlidingWindowRateLimitMiddleware, createSSEHeaders, createTimingMiddleware, createToolRoutes, DataStreamResponse, \n// Error Constants\nErrorCategory, ErrorRecoveryStrategies, ErrorSeverity, ExpressServerAdapter, FastifyServerAdapter, generateOpenAPIFromConfig, generateOpenAPISpec, HandlerError, HonoServerAdapter, InMemoryCacheStore, InMemoryRateLimitStore, InvalidAuthenticationError, KoaServerAdapter, MissingDependencyError, NotRunningError, \n// OpenAPI\nOpenAPIGenerator, pipeAsyncIterableToDataStream, RateLimitError, RouteConflictError, RouteNotFoundError, registerAllRoutes, \n// Errors\nServerAdapterError, ServerAdapterErrorCode, ServerAdapterFactory, ServerNameParamSchema, ServerRateLimitError, ServerStartError, ServerStopError, ServerValidationError, SessionIdParamSchema, StreamAbortedError, StreamingError, TimeoutError, ToolArgumentsSchema, ToolExecuteRequestSchema, ToolNameParamSchema, ToolSearchQuerySchema, ValidationError, validateParams, validateQuery, validateRequest, WebSocketConnectionError, \n// WebSocket\nWebSocketConnectionManager, WebSocketError, WebSocketMessageRouter, wrapError, } from \"./server/index.js\";\n/**\n * RAG (Retrieval-Augmented Generation) Document Processing\n *\n * Comprehensive RAG system with document loading, chunking, embedding,\n * retrieval, and context assembly capabilities.\n *\n * @example\n * ```typescript\n * import {\n *   MDocument,\n *   loadDocument,\n *   RAGPipeline,\n *   ChunkerRegistry\n * } from '@juspay/neurolink';\n *\n * // Load and process a document\n * const doc = await loadDocument('/path/to/document.md');\n * await doc.chunk({ strategy: 'markdown', config: { maxSize: 1000 } });\n *\n * // Use the full RAG pipeline\n * const pipeline = new RAGPipeline({\n *   embeddingModel: { provider: 'openai', modelName: 'text-embedding-3-small' },\n *   generationModel: { provider: 'openai', modelName: 'gpt-4o-mini' }\n * });\n * await pipeline.ingest(['./docs/*.md']);\n * const response = await pipeline.query('What are the key features?');\n * console.log(response.answer);\n * ```\n */\nexport { \n// Pipeline\nassembleContext, \n// Reranker\nbatchRerank, \n// Chunking\nCharacterChunker, ChunkerRegistry, CohereRelevanceScorer, CrossEncoderReranker, \n// Document Processing\nCSVLoader, chunkText, createChunker, createContextWindow, \n// Retrieval\ncreateHybridSearch, createRAGPipeline, createVectorQueryTool, \n// Resilience\nexecuteWithCircuitBreaker, \n// Metadata Extraction\nextractMetadata, formatContextWithCitations, \n// Graph RAG\nGraphRAG, getAvailableStrategies, getCircuitBreaker, getDefaultChunkerConfig, getRecommendedStrategy, HTMLChunker, HTMLLoader, InMemoryBM25Index, InMemoryVectorStore, JSONChunker as RAGJSONChunker, JSONLoader, LaTeXChunker, LLMMetadataExtractor, linearCombination, loadDocument, loadDocuments, MarkdownChunker, MarkdownLoader, MDocument, PDFLoader, \n// RAG Integration for generate/stream\nprepareRAGTool, processDocument, RAGCircuitBreaker, RAGCircuitBreakerManager, RAGPipeline, RAGRetryHandler, RecursiveChunker, ragCircuitBreakerManager, reciprocalRankFusion, rerank, SemanticChunker, SentenceChunker, simpleRerank, summarizeContext, TextLoader, TokenChunker, WebLoader, } from \"./rag/index.js\";\n"]}