import { Rag, RagConfig } from "./contracts/rag-config.type.mjs";

//#region ../ai/src/rag/rag.d.ts
/**
 * Create a RAG pipeline: **chunk → embed → vector store → retrieve →
 * rerank → cite**, reusing the app's `ai.embedder` for embedding, a
 * `@warlock.js/cache` `CacheDriver` as the vector store, and the
 * composite-as-tool engine to expose retrieval as a tool.
 *
 * Resolution is loud at construction (mirroring `memory()`):
 * - `embedder` is required — a provider with no embedder must be caught
 *   here, not at first index.
 * - `store` falls back to `ai.config({ defaultStore })`; if neither
 *   resolves, construction throws.
 *
 * `retrieve()` is return-only — it never auto-injects into a prompt; the
 * caller formats the cited chunks (or uses `asTool()` for the agent loop).
 * The reranker is OFF by default (cosine-only) unless `config.reranker`
 * is set.
 *
 * @example
 * import { ai } from "@warlock.js/ai";
 * import { MemoryCacheDriver } from "@warlock.js/cache";
 *
 * const kb = ai.rag({
 *   name: "docs",
 *   embedder: openai.embedder({ name: "text-embedding-3-small" }),
 *   store: new MemoryCacheDriver(),
 *   chunk: { type: "markdown", size: 800, overlap: 120 },
 * });
 *
 * await kb.index([{ id: "guide", text: longMarkdown, metadata: { url: "/guide" } }]);
 * const { chunks } = await kb.retrieve("how do I configure caching?", { topK: 4 });
 */
declare function rag(config: RagConfig): Rag;
//#endregion
export { rag };
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