import { type EmbeddingsInterface, Embeddings } from "@langchain/core/embeddings";
import { BaseStore } from "@langchain/core/stores";
import { AsyncCallerParams } from "@langchain/core/utils/async_caller";
/**
 * Interface for the fields required to initialize an instance of the
 * CacheBackedEmbeddings class.
 */
export interface CacheBackedEmbeddingsFields extends AsyncCallerParams {
    underlyingEmbeddings: EmbeddingsInterface;
    documentEmbeddingStore: BaseStore<string, number[]>;
}
/**
 * Interface for caching results from embedding models.
 *
 * The interface allows works with any store that implements
 * the abstract store interface accepting keys of type str and values of list of
 * floats.
 *
 * If need be, the interface can be extended to accept other implementations
 * of the value serializer and deserializer, as well as the key encoder.
 * @example
 * ```typescript
 * const underlyingEmbeddings = new OpenAIEmbeddings();
 *
 * const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
 *   underlyingEmbeddings,
 *   new ConvexKVStore({ ctx }),
 *   {
 *     namespace: underlyingEmbeddings.modelName,
 *   },
 * );
 *
 * const loader = new TextLoader("./state_of_the_union.txt");
 * const rawDocuments = await loader.load();
 * const splitter = new RecursiveCharacterTextSplitter({
 *   chunkSize: 1000,
 *   chunkOverlap: 0,
 * });
 * const documents = await splitter.splitDocuments(rawDocuments);
 *
 * let time = Date.now();
 * const vectorstore = await ConvexVectorStore.fromDocuments(
 *   documents,
 *   cacheBackedEmbeddings,
 *   { ctx },
 * );
 * console.log(`Initial creation time: ${Date.now() - time}ms`);
 *
 * time = Date.now();
 * const vectorstore2 = await ConvexVectorStore.fromDocuments(
 *   documents,
 *   cacheBackedEmbeddings,
 *   { ctx },
 * );
 * console.log(`Cached creation time: ${Date.now() - time}ms`);
 *
 * ```
 */
export declare class CacheBackedEmbeddings extends Embeddings {
    protected underlyingEmbeddings: EmbeddingsInterface;
    protected documentEmbeddingStore: BaseStore<string, number[]>;
    constructor(fields: CacheBackedEmbeddingsFields);
    /**
     * Embed query text.
     *
     * This method does not support caching at the moment.
     *
     * Support for caching queries is easy to implement, but might make
     * sense to hold off to see the most common patterns.
     *
     * If the cache has an eviction policy, we may need to be a bit more careful
     * about sharing the cache between documents and queries. Generally,
     * one is OK evicting query caches, but document caches should be kept.
     *
     * @param document The text to embed.
     * @returns The embedding for the given text.
     */
    embedQuery(document: string): Promise<number[]>;
    /**
     * Embed a list of texts.
     *
     * The method first checks the cache for the embeddings.
     * If the embeddings are not found, the method uses the underlying embedder
     * to embed the documents and stores the results in the cache.
     *
     * @param documents
     * @returns A list of embeddings for the given texts.
     */
    embedDocuments(documents: string[]): Promise<number[][]>;
    /**
     * Create a new CacheBackedEmbeddings instance from another embeddings instance
     * and a storage instance.
     * @param underlyingEmbeddings Embeddings used to populate the cache for new documents.
     * @param documentEmbeddingStore Stores raw document embedding values. Keys are hashes of the document content.
     * @param options.namespace Optional namespace for store keys.
     * @returns A new CacheBackedEmbeddings instance.
     */
    static fromBytesStore(underlyingEmbeddings: EmbeddingsInterface, documentEmbeddingStore: BaseStore<string, Uint8Array>, options?: {
        namespace?: string;
    }): CacheBackedEmbeddings;
}