import * as uuid from "uuid";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { AsyncCaller, } from "@langchain/core/utils/async_caller";
const CONCURRENT_UPSERT_LIMIT = 1000;
/**
 * The main class that extends the 'VectorStore' class. It provides
 * methods for interacting with Upstash index, such as adding documents,
 * deleting documents, performing similarity search and more.
 */
export class UpstashVectorStore extends VectorStore {
    _vectorstoreType() {
        return "upstash";
    }
    constructor(embeddings, args) {
        super(embeddings, args);
        Object.defineProperty(this, "index", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "caller", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "useUpstashEmbeddings", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "filter", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "namespace", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        // Special case where the embeddings instance is a FakeEmbeddings instance. In this case, we need to disable "instanceof" rule.
        // eslint-disable-next-line no-instanceof/no-instanceof
        if (embeddings instanceof FakeEmbeddings) {
            this.useUpstashEmbeddings = true;
        }
        const { index, namespace, ...asyncCallerArgs } = args;
        this.index = index;
        this.caller = new AsyncCaller(asyncCallerArgs);
        this.filter = args.filter;
        this.namespace = namespace;
    }
    /**
     * This method adds documents to Upstash database. Documents are first converted to vectors
     * using the provided embeddings instance, and then upserted to the database.
     * @param documents Array of Document objects to be added to the database.
     * @param options Optional object containing array of ids for the documents.
     * @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
     */
    async addDocuments(documents, options) {
        const texts = documents.map(({ pageContent }) => pageContent);
        if (this.useUpstashEmbeddings || options?.useUpstashEmbeddings) {
            return this._addData(documents, options);
        }
        const embeddings = await this.embeddings.embedDocuments(texts);
        return this.addVectors(embeddings, documents, options);
    }
    /**
     * This method adds the provided vectors to Upstash database.
     * @param vectors  Array of vectors to be added to the Upstash database.
     * @param documents Array of Document objects, each associated with a vector.
     * @param options Optional object containing the array of ids foor the vectors.
     * @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
     */
    async addVectors(vectors, documents, options) {
        const documentIds = options?.ids ?? Array.from({ length: vectors.length }, () => uuid.v4());
        const upstashVectors = vectors.map((vector, index) => {
            const metadata = {
                _pageContentLC: documents[index].pageContent,
                ...documents[index].metadata,
            };
            const id = documentIds[index];
            return {
                id,
                vector,
                metadata,
            };
        });
        const namespace = this.index.namespace(this.namespace ?? "");
        const vectorChunks = chunkArray(upstashVectors, CONCURRENT_UPSERT_LIMIT);
        const batchRequests = vectorChunks.map((chunk) => this.caller.call(async () => namespace.upsert(chunk)));
        await Promise.all(batchRequests);
        return documentIds;
    }
    /**
     * This method adds the provided documents to Upstash database. The pageContent of the documents will be embedded by Upstash Embeddings.
     * @param documents Array of Document objects to be added to the Upstash database.
     * @param options Optional object containing the array of ids for the documents.
     * @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
     */
    async _addData(documents, options) {
        const documentIds = options?.ids ?? Array.from({ length: documents.length }, () => uuid.v4());
        const upstashVectorsWithData = documents.map((document, index) => {
            const metadata = {
                _pageContentLC: documents[index].pageContent,
                ...documents[index].metadata,
            };
            const id = documentIds[index];
            return {
                id,
                data: document.pageContent,
                metadata,
            };
        });
        const namespace = this.index.namespace(this.namespace ?? "");
        const vectorChunks = chunkArray(upstashVectorsWithData, CONCURRENT_UPSERT_LIMIT);
        const batchRequests = vectorChunks.map((chunk) => this.caller.call(async () => namespace.upsert(chunk)));
        await Promise.all(batchRequests);
        return documentIds;
    }
    /**
     * This method deletes documents from the Upstash database. You can either
     * provide the target ids, or delete all vectors in the database.
     * @param params Object containing either array of ids of the documents or boolean deleteAll.
     * @returns Promise that resolves when the specified documents have been deleted from the database.
     */
    async delete(params) {
        const namespace = this.index.namespace(this.namespace ?? "");
        if (params.deleteAll) {
            await namespace.reset();
        }
        else if (params.ids) {
            await namespace.delete(params.ids);
        }
    }
    async _runUpstashQuery(query, k, filter, options) {
        let queryResult = [];
        const namespace = this.index.namespace(this.namespace ?? "");
        if (typeof query === "string") {
            queryResult = await namespace.query({
                data: query,
                topK: k,
                includeMetadata: true,
                filter,
                ...options,
            });
        }
        else {
            queryResult = await namespace.query({
                vector: query,
                topK: k,
                includeMetadata: true,
                filter,
                ...options,
            });
        }
        return queryResult;
    }
    /**
     * This method performs a similarity search in the Upstash database
     * over the existing vectors.
     * @param query Query vector for the similarity search.
     * @param k The number of similar vectors to return as result.
     * @returns Promise that resolves with an array of tuples, each containing
     *  Document object and similarity score. The length of the result will be
     *  maximum of 'k' and vectors in the index.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        const results = await this._runUpstashQuery(query, k, filter);
        const searchResult = results.map((res) => {
            const { _pageContentLC, ...metadata } = (res.metadata ??
                {});
            return [
                new Document({
                    metadata,
                    pageContent: _pageContentLC,
                }),
                res.score,
            ];
        });
        return searchResult;
    }
    /**
     * This method creates a new UpstashVector instance from an array of texts.
     * The texts are initially converted to Document instances and added to Upstash
     * database.
     * @param texts The texts to create the documents from.
     * @param metadatas The metadata values associated with the texts.
     * @param embeddings Embedding interface of choice, to create the text embeddings.
     * @param dbConfig Object containing the Upstash database configs.
     * @returns Promise that resolves with a new UpstashVector instance.
     */
    static async fromTexts(texts, metadatas, embeddings, dbConfig) {
        const docs = [];
        for (let i = 0; i < texts.length; i += 1) {
            const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
            const newDocument = new Document({
                pageContent: texts[i],
                metadata,
            });
            docs.push(newDocument);
        }
        return this.fromDocuments(docs, embeddings, dbConfig);
    }
    /**
     * This method creates a new UpstashVector instance from an array of Document instances.
     * @param docs The docs to be added to Upstash database.
     * @param embeddings Embedding interface of choice, to create the embeddings.
     * @param dbConfig Object containing the Upstash database configs.
     * @returns Promise that resolves with a new UpstashVector instance
     */
    static async fromDocuments(docs, embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * This method creates a new UpstashVector instance from an existing index.
     * @param embeddings Embedding interface of the choice, to create the embeddings.
     * @param dbConfig Object containing the Upstash database configs.
     * @returns
     */
    static async fromExistingIndex(embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        return instance;
    }
}