import { Document } from "../../documents/document.js";
import { EmbeddingsInterface } from "../../embeddings.js";
import { VectorStore } from "../../vectorstores.js";
import { cosine } from "../ml-distance/similarities.js";

//#region src/utils/testing/vectorstores.d.ts
/**
 * Interface representing a vector in memory. It includes the content
 * (text), the corresponding embedding (vector), and any associated
 * metadata.
 */
interface MemoryVector {
  content: string;
  embedding: number[];
  metadata: Record<string, any>;
}
/**
 * Interface for the arguments that can be passed to the
 * `FakeVectorStore` constructor. It includes an optional `similarity`
 * function.
 */
interface FakeVectorStoreArgs {
  similarity?: typeof cosine;
}
/**
 * Class that extends `VectorStore` to store vectors in memory. Provides
 * methods for adding documents, performing similarity searches, and
 * creating instances from texts, documents, or an existing index.
 */
declare class FakeVectorStore extends VectorStore {
  FilterType: (doc: Document) => boolean;
  memoryVectors: MemoryVector[];
  similarity: typeof cosine;
  _vectorstoreType(): string;
  constructor(embeddings: EmbeddingsInterface, {
    similarity,
    ...rest
  }?: FakeVectorStoreArgs);
  /**
   * Method to add documents to the memory vector store. It extracts the
   * text from each document, generates embeddings for them, and adds the
   * resulting vectors to the store.
   * @param documents Array of `Document` instances to be added to the store.
   * @returns Promise that resolves when all documents have been added.
   */
  addDocuments(documents: Document[]): Promise<void>;
  /**
   * Method to add vectors to the memory vector store. It creates
   * `MemoryVector` instances for each vector and document pair and adds
   * them to the store.
   * @param vectors Array of vectors to be added to the store.
   * @param documents Array of `Document` instances corresponding to the vectors.
   * @returns Promise that resolves when all vectors have been added.
   */
  addVectors(vectors: number[][], documents: Document[]): Promise<void>;
  /**
   * Method to perform a similarity search in the memory vector store. It
   * calculates the similarity between the query vector and each vector in
   * the store, sorts the results by similarity, and returns the top `k`
   * results along with their scores.
   * @param query Query vector to compare against the vectors in the store.
   * @param k Number of top results to return.
   * @param filter Optional filter function to apply to the vectors before performing the search.
   * @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.
   */
  similaritySearchVectorWithScore(query: number[], k: number, filter?: this["FilterType"]): Promise<[Document, number][]>;
  /**
   * Static method to create a `FakeVectorStore` instance from an array of
   * texts. It creates a `Document` for each text and metadata pair, and
   * adds them to the store.
   * @param texts Array of texts to be added to the store.
   * @param metadatas Array or single object of metadata corresponding to the texts.
   * @param embeddings `Embeddings` instance used to generate embeddings for the texts.
   * @param dbConfig Optional `FakeVectorStoreArgs` to configure the `FakeVectorStore` instance.
   * @returns Promise that resolves with a new `FakeVectorStore` instance.
   */
  static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig?: FakeVectorStoreArgs): Promise<FakeVectorStore>;
  /**
   * Static method to create a `FakeVectorStore` instance from an array of
   * `Document` instances. It adds the documents to the store.
   * @param docs Array of `Document` instances to be added to the store.
   * @param embeddings `Embeddings` instance used to generate embeddings for the documents.
   * @param dbConfig Optional `FakeVectorStoreArgs` to configure the `FakeVectorStore` instance.
   * @returns Promise that resolves with a new `FakeVectorStore` instance.
   */
  static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig?: FakeVectorStoreArgs): Promise<FakeVectorStore>;
  /**
   * Static method to create a `FakeVectorStore` instance from an existing
   * index. It creates a new `FakeVectorStore` instance without adding any
   * documents or vectors.
   * @param embeddings `Embeddings` instance used to generate embeddings for the documents.
   * @param dbConfig Optional `FakeVectorStoreArgs` to configure the `FakeVectorStore` instance.
   * @returns Promise that resolves with a new `FakeVectorStore` instance.
   */
  static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig?: FakeVectorStoreArgs): Promise<FakeVectorStore>;
}
//#endregion
export { FakeVectorStore, FakeVectorStoreArgs };
//# sourceMappingURL=vectorstores.d.ts.map