@dooor-ai/toolkit/dist/rag/helpers.d.ts

Guards, Evals & Observability for AI applications - works seamlessly with LangChain/LangGraph

/**
 * RAG helper functions for DOOOR AI Toolkit
 */
import { RAGContext } from "./context";
import { RAGMetadata, RAGResult } from "./types";
export interface RAGRetrievalResult {
    context: string;
    results: RAGResult[];
    metadata: RAGMetadata;
}
/**
 * Retrieve relevant context using RAG
 *
 * @param query - The user query
 * @param ragContext - RAG configuration (files, documents, embeddings, strategy)
 * @returns Retrieved context and metadata
 *
 * @example
 * ```typescript
 * const ragContext = new RAGContext({
 *   files: [pdfFile],
 *   embeddingProvider: "prod-gemini",
 *   strategy: RAGStrategy.HYDE,
 * });
 *
 * const { context, metadata } = await retrieveContext("How to authenticate?", ragContext);
 *
 * const result = await llm.invoke([
 *   { role: "user", content: `Context:\n${context}\n\nQuestion: How to authenticate?` }
 * ]);
 * ```
 */
export declare function retrieveContext(query: string, ragContext: RAGContext): Promise<RAGRetrievalResult>;
/**
 * Build a prompt with RAG context injected
 *
 * @param query - The user query
 * @param ragContext - RAG configuration
 * @returns Prompt with context injected
 *
 * @example
 * ```typescript
 * const prompt = await buildRAGPrompt("How to authenticate?", ragContext);
 * const result = await llm.invoke([{ role: "user", content: prompt }]);
 * ```
 */
export declare function buildRAGPrompt(query: string, ragContext: RAGContext): Promise<string>;
//# sourceMappingURL=helpers.d.ts.map