import { Document, Filter, MongoClient } from "mongodb";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
import { Document as Document$1, DocumentInterface } from "@langchain/core/documents";
import { EmbeddingsInterface } from "@langchain/core/embeddings";

//#region src/azure_documentdb.d.ts
/** Azure DocumentDB Similarity type. */
declare const AzureDocumentDBSimilarityType: {
  /** Cosine similarity */readonly COS: "COS"; /** Inner - product */
  readonly IP: "IP"; /** Euclidian distance */
  readonly L2: "L2";
};
/** Azure DocumentDB Similarity type. */
type AzureDocumentDBSimilarityType = (typeof AzureDocumentDBSimilarityType)[keyof typeof AzureDocumentDBSimilarityType];
/** Azure DocumentDB Index Options. */
type AzureDocumentDBIndexOptions = {
  /** Skips automatic index creation. */readonly skipCreate?: boolean;
  readonly indexType?: "ivf" | "hnsw" | "diskann"; /** Number of clusters that the inverted file (IVF) index uses to group the vector data. */
  readonly numLists?: number; /** Number of dimensions for vector similarity. */
  readonly dimensions?: number; /** Similarity metric to use with the IVF index. */
  readonly similarity?: AzureDocumentDBSimilarityType; /** The max number of connections per layer with the HNSW index. */
  readonly m?: number; /** The size of the dynamic candidate list for constructing the graph with the HNSW index. */
  readonly efConstruction?: number; /** Max number of neighbors with the DiskANN index */
  readonly maxDegree?: number; /** L value for index building with the DiskANN index */
  readonly lBuild?: number; /** L value for index searching with the DiskANN index */
  readonly lSearch?: number;
};
/** Azure DocumentDB delete Parameters. */
type AzureDocumentDBDeleteParams = {
  /** List of IDs for the documents to be removed. */readonly ids?: string | string[]; /** MongoDB filter object or list of IDs for the documents to be removed. */
  readonly filter?: Filter<Document>;
};
/** Configuration options for the `AzureDocumentDBVectorStore` constructor. */
interface AzureDocumentDBConfig {
  readonly client?: MongoClient;
  readonly connectionString?: string;
  readonly databaseName?: string;
  readonly collectionName?: string;
  readonly indexName?: string;
  readonly textKey?: string;
  readonly embeddingKey?: string;
  readonly indexOptions?: AzureDocumentDBIndexOptions;
}
/**
 * Azure DocumentDB vector store.
 * To use this, you should have both:
 * - the `mongodb` NPM package installed
 * - a connection string associated with an Azure DocumentDB cluster
 *
 * You do not need to create a database or collection, it will be created
 * automatically.
 *
 * You also need an index on the collection, which is by default created
 * automatically using the `createIndex` method.
 */
declare class AzureDocumentDBVectorStore extends VectorStore {
  get lc_secrets(): {
    [key: string]: string;
  };
  private connectPromise;
  private initPromise?;
  private readonly client;
  private database;
  private collection;
  readonly indexName: string;
  readonly textKey: string;
  readonly embeddingKey: string;
  private readonly indexOptions;
  /**
   * Initializes the AzureDocumentDBVectorStore.
   * Connect the client to the database and create the container, creating them if needed.
   * @returns A promise that resolves when the AzureDocumentDBVectorStore has been initialized.
   */
  initialize: () => Promise<void>;
  _vectorstoreType(): string;
  constructor(embeddings: EmbeddingsInterface, dbConfig: AzureDocumentDBConfig);
  /**
   * Checks if the specified index name during instance construction exists
   * on the collection.
   * @returns A promise that resolves to a boolean indicating if the index exists.
   */
  checkIndexExists(): Promise<boolean>;
  /**
   * Deletes the index specified during instance construction if it exists.
   * @returns A promise that resolves when the index has been deleted.
   */
  deleteIndex(): Promise<void>;
  /**
   * Creates an index on the collection with the specified index name during
   * instance construction.
   *
   * Setting the numLists parameter correctly is important for achieving good
   * accuracy and performance.
   * Since the vector store uses IVF as the indexing strategy, you should
   * create the index only after you have loaded a large enough sample
   * documents to ensure that the centroids for the respective buckets are
   * fairly distributed.
   *
   * We recommend that numLists is set to documentCount/1000 for up to
   * 1 million documents and to sqrt(documentCount) for more than 1 million
   * documents.
   * As the number of items in your database grows, you should tune numLists
   * to be larger in order to achieve good latency performance for vector
   * search.
   *
   * If you're experimenting with a new scenario or creating a small demo,
   * you can start with numLists set to 1 to perform a brute-force search
   * across all vectors.
   * This should provide you with the most accurate results from the vector
   * search, however be aware that the search speed and latency will be slow.
   * After your initial setup, you should go ahead and tune the numLists
   * parameter using the above guidance.
   * @param numLists This integer is the number of clusters that the inverted
   *    file (IVF) index uses to group the vector data.
   *    We recommend that numLists is set to documentCount/1000 for up to
   *    1 million documents and to sqrt(documentCount) for more than 1 million
   *    documents.
   *    Using a numLists value of 1 is akin to performing brute-force search,
   *    which has limited performance
   * @param indexType Index Type for Azure DocumentDB index.
   * @param dimensions Number of dimensions for vector similarity.
   *    The maximum number of supported dimensions is 2000.
   *    If no number is provided, it will be determined automatically by
   *    embedding a short text.
   * @param similarity Similarity metric to use with the IVF index.
   *    Possible options are:
   *    - AzureDocumentDBSimilarityType.COS (cosine distance)
   *    - AzureDocumentDBSimilarityType.L2 (Euclidean distance)
   *    - AzureDocumentDBSimilarityType.IP (inner product)
   * @returns A promise that resolves when the index has been created.
   */
  createIndex(dimensions?: number | undefined, indexType?: "ivf" | "hnsw" | "diskann", similarity?: AzureDocumentDBSimilarityType): Promise<void>;
  /**
   * Removes specified documents from the AzureDocumentDBVectorStore.
   * If no IDs or filter are specified, all documents will be removed.
   * @param params Parameters for the delete operation.
   * @returns A promise that resolves when the documents have been removed.
   */
  delete(params?: AzureDocumentDBDeleteParams | string[]): Promise<void>;
  /**
   * Closes any newly instantiated Azure DocumentDB client.
   * If the client was passed in the constructor, it will not be closed.
   * @returns A promise that resolves when any newly instantiated Azure
   *     DocumentDB client has been closed.
   */
  close(): Promise<void>;
  /**
   * Method for adding vectors to the AzureDocumentDBVectorStore.
   * @param vectors Vectors to be added.
   * @param documents Corresponding documents to be added.
   * @returns A promise that resolves to the added documents IDs.
   */
  addVectors(vectors: number[][], documents: DocumentInterface[]): Promise<string[]>;
  /**
   * Method for adding documents to the AzureDocumentDBVectorStore. It first converts
   * the documents to texts and then adds them as vectors.
   * @param documents The documents to add.
   * @returns A promise that resolves to the added documents IDs.
   */
  addDocuments(documents: DocumentInterface[]): Promise<string[]>;
  /**
   * Method that performs a similarity search on the vectors stored in the
   * collection. It returns a list of documents and their corresponding
   * similarity scores.
   * @param queryVector Query vector for the similarity search.
   * @param k=4 Number of nearest neighbors to return.
   * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
   */
  similaritySearchVectorWithScore(queryVector: number[], k: number, indexType?: "ivf" | "hnsw" | "diskann"): Promise<[Document$1, number][]>;
  /**
   * Return documents selected using the maximal marginal relevance.
   * Maximal marginal relevance optimizes for similarity to the query AND
   * diversity among selected documents.
   * @param query Text to look up documents similar to.
   * @param options.k Number of documents to return.
   * @param options.fetchK=20 Number of documents to fetch before passing to
   *     the MMR algorithm.
   * @param options.lambda=0.5 Number between 0 and 1 that determines the
   *     degree of diversity among the results, where 0 corresponds to maximum
   *     diversity and 1 to minimum diversity.
   * @returns List of documents selected by maximal marginal relevance.
   */
  maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document$1[]>;
  maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>, indexType: "ivf" | "hnsw" | "diskann"): Promise<Document$1[]>;
  private init;
  /**
   * Static method to create an instance of AzureDocumentDBVectorStore from a
   * list of texts. It first converts the texts to vectors and then adds
   * them to the collection.
   * @param texts List of texts to be converted to vectors.
   * @param metadatas Metadata for the texts.
   * @param embeddings Embeddings to be used for conversion.
   * @param dbConfig Database configuration for Azure DocumentDB.
   * @returns Promise that resolves to a new instance of AzureDocumentDBVectorStore.
   */
  static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: AzureDocumentDBConfig): Promise<AzureDocumentDBVectorStore>;
  /**
   * Static method to create an instance of AzureDocumentDBVectorStore from a
   * list of documents. It first converts the documents to vectors and then
   * adds them to the collection.
   * @param docs List of documents to be converted to vectors.
   * @param embeddings Embeddings to be used for conversion.
   * @param dbConfig Database configuration for Azure DocumentDB.
   * @returns Promise that resolves to a new instance of AzureDocumentDBVectorStore.
   */
  static fromDocuments(docs: Document$1[], embeddings: EmbeddingsInterface, dbConfig: AzureDocumentDBConfig): Promise<AzureDocumentDBVectorStore>;
}
/**
 * @deprecated Use `AzureDocumentDBSimilarityType` instead. This alias will be removed in a future version.
 */
declare const AzureCosmosDBMongoDBSimilarityType: {
  /** Cosine similarity */readonly COS: "COS"; /** Inner - product */
  readonly IP: "IP"; /** Euclidian distance */
  readonly L2: "L2";
};
/**
 * @deprecated Use `AzureDocumentDBSimilarityType` instead. This alias will be removed in a future version.
 */
type AzureCosmosDBMongoDBSimilarityType = AzureDocumentDBSimilarityType;
/**
 * @deprecated Use `AzureDocumentDBIndexOptions` instead. This alias will be removed in a future version.
 */
type AzureCosmosDBMongoDBIndexOptions = AzureDocumentDBIndexOptions;
/**
 * @deprecated Use `AzureDocumentDBDeleteParams` instead. This alias will be removed in a future version.
 */
type AzureCosmosDBMongoDBDeleteParams = AzureDocumentDBDeleteParams;
/**
 * @deprecated Use `AzureDocumentDBConfig` instead. This alias will be removed in a future version.
 */
type AzureCosmosDBMongoDBConfig = AzureDocumentDBConfig;
/**
 * @deprecated Use `AzureDocumentDBVectorStore` instead. This alias will be removed in a future version.
 */
declare const AzureCosmosDBMongoDBVectorStore: typeof AzureDocumentDBVectorStore;
/**
 * @deprecated Use `AzureDocumentDBVectorStore` instead. This alias will be removed in a future version.
 */
type AzureCosmosDBMongoDBVectorStore = AzureDocumentDBVectorStore;
//#endregion
export { AzureCosmosDBMongoDBConfig, AzureCosmosDBMongoDBDeleteParams, AzureCosmosDBMongoDBIndexOptions, AzureCosmosDBMongoDBSimilarityType, AzureCosmosDBMongoDBVectorStore, AzureDocumentDBConfig, AzureDocumentDBDeleteParams, AzureDocumentDBIndexOptions, AzureDocumentDBSimilarityType, AzureDocumentDBVectorStore };
//# sourceMappingURL=azure_documentdb.d.ts.map