/**
 * embeddings.ts
 *
 * OpenAI text-embedding-3-small client for wiki page vectors.
 * Used at ingest time (upsertPage) and at query time (hybrid search).
 *
 * Cost: $0.020 / 1M tokens (text-embedding-3-small).
 * Falls back silently when OPENAI_API_KEY is unset so the server
 * continues to function with FTS-only search.
 */
export declare const EMBEDDING_MODEL = "text-embedding-3-small";
export declare const EMBEDDING_DIMS = 1536;
export declare function isEmbeddingEnabled(): boolean;
/**
 * Generate a single embedding for `text`.
 * Returns null when OPENAI_API_KEY is unset or the API call fails.
 * Text is truncated to ~8000 tokens before sending (model max is 8192).
 */
export declare function generateEmbedding(text: string): Promise<number[] | null>;
/**
 * Generate embeddings for a batch of texts.
 * OpenAI allows up to 2048 inputs per request; callers should chunk larger batches.
 * Returns an array of the same length; failed entries are null.
 */
export declare function generateEmbeddings(texts: string[]): Promise<Array<number[] | null>>;
/**
 * Build the text to embed for a page: title + summary + (first 2000 chars of content).
 * Keeping content short reduces token cost while preserving semantic signal.
 */
export declare function buildEmbeddingInput(title: string, summary: string, content: string): string;
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