@juspay/neurolink/dist/lib/rag/ragIntegration.d.ts

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/**
 * RAG Integration for generate() and stream()
 *
 * Provides automatic RAG pipeline setup when `rag` config is provided
 * in GenerateOptions or StreamOptions. Handles file loading, chunking,
 * embedding generation, vector storage, and tool creation internally
 * so developers only need to pass `rag: { files: [...] }`.
 */
import type { RAGConfig, VectorQueryResult, RAGPreparedTool } from "../types/index.js";
/**
 * Generate deterministic embeddings for chunks.
 * Combines character-frequency (40%) with word-level hash features (60%)
 * for better semantic discrimination than pure character frequency.
 * When a real embedding provider is configured, it will be used instead.
 */
declare function generateSimpleEmbedding(text: string, dimension: number): number[];
/**
 * Diversify retrieval results via round-robin across source files.
 * Ensures at least one chunk per source file appears in the top-K results,
 * preventing any single file from dominating retrieval.
 */
declare function diversifyResults(results: VectorQueryResult[], topK: number): VectorQueryResult[];
/**
 * Prepare RAG tools from the provided configuration.
 *
 * This function:
 * 1. Loads and reads all specified files
 * 2. Chunks them using the configured (or auto-detected) strategy
 * 3. Generates embeddings for each chunk
 * 4. Stores them in an in-memory vector store
 * 5. Creates a tool the AI model can use to search the documents
 *
 * @param ragConfig - RAG configuration from generate/stream options
 * @param fallbackProvider - Provider to use for embeddings if not specified in ragConfig
 * @returns Prepared RAG tool to inject into the tools record
 */
export declare function prepareRAGTool(ragConfig: RAGConfig, fallbackProvider?: string): Promise<RAGPreparedTool>;
/** @internal Exported for testing only */
export { generateSimpleEmbedding as _generateSimpleEmbedding };
/** @internal Exported for testing only */
export { diversifyResults as _diversifyResults };