"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
exports.CloudflareVectorizeStore = void 0;
const uuid = __importStar(require("uuid"));
const vectorstores_1 = require("@langchain/core/vectorstores");
const documents_1 = require("@langchain/core/documents");
const async_caller_1 = require("@langchain/core/utils/async_caller");
const chunk_array_1 = require("@langchain/core/utils/chunk_array");
/**
 * @deprecated Install and import from "@langchain/cloudflare" instead.
 *
 * Class that extends the VectorStore class and provides methods to
 * interact with the Cloudflare Vectorize vector database.
 */
class CloudflareVectorizeStore extends vectorstores_1.VectorStore {
    _vectorstoreType() {
        return "cloudflare_vectorize";
    }
    constructor(embeddings, args) {
        super(embeddings, args);
        Object.defineProperty(this, "textKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "namespace", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        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
        });
        this.embeddings = embeddings;
        const { index, textKey, ...asyncCallerArgs } = args;
        if (!index) {
            throw new Error("Must supply a Vectorize index binding, eg { index: env.VECTORIZE }");
        }
        this.index = index;
        this.textKey = textKey ?? "text";
        this.caller = new async_caller_1.AsyncCaller({
            maxConcurrency: 6,
            maxRetries: 0,
            ...asyncCallerArgs,
        });
    }
    /**
     * Method that adds documents to the Vectorize database.
     * @param documents Array of documents to add.
     * @param options Optional ids for the documents.
     * @returns Promise that resolves with the ids of the added documents.
     */
    async addDocuments(documents, options) {
        const texts = documents.map(({ pageContent }) => pageContent);
        return this.addVectors(await this.embeddings.embedDocuments(texts), documents, options);
    }
    /**
     * Method that adds vectors to the Vectorize database.
     * @param vectors Array of vectors to add.
     * @param documents Array of documents associated with the vectors.
     * @param options Optional ids for the vectors.
     * @returns Promise that resolves with the ids of the added vectors.
     */
    async addVectors(vectors, documents, options) {
        const ids = Array.isArray(options) ? options : options?.ids;
        const documentIds = ids == null ? documents.map(() => uuid.v4()) : ids;
        const vectorizeVectors = vectors.map((values, idx) => {
            const metadata = {
                ...documents[idx].metadata,
                [this.textKey]: documents[idx].pageContent,
            };
            return {
                id: documentIds[idx],
                metadata,
                values,
            };
        });
        // Stick to a limit of 500 vectors per upsert request
        const chunkSize = 500;
        const chunkedVectors = (0, chunk_array_1.chunkArray)(vectorizeVectors, chunkSize);
        const batchRequests = chunkedVectors.map((chunk) => this.caller.call(async () => this.index.upsert(chunk)));
        await Promise.all(batchRequests);
        return documentIds;
    }
    /**
     * Method that deletes vectors from the Vectorize database.
     * @param params Parameters for the delete operation.
     * @returns Promise that resolves when the delete operation is complete.
     */
    async delete(params) {
        const batchSize = 1000;
        const batchedIds = (0, chunk_array_1.chunkArray)(params.ids, batchSize);
        const batchRequests = batchedIds.map((batchIds) => this.caller.call(async () => this.index.deleteByIds(batchIds)));
        await Promise.all(batchRequests);
    }
    /**
     * Method that performs a similarity search in the Vectorize database and
     * returns the results along with their scores.
     * @param query Query vector for the similarity search.
     * @param k Number of top results to return.
     * @returns Promise that resolves with an array of documents and their scores.
     */
    async similaritySearchVectorWithScore(query, k) {
        const results = await this.index.query(query, {
            returnValues: true,
            topK: k,
        });
        const result = [];
        if (results.matches) {
            for (const res of results.matches) {
                const { [this.textKey]: pageContent, ...metadata } = res.metadata ?? {};
                result.push([
                    new documents_1.Document({ metadata, pageContent: pageContent }),
                    res.score,
                ]);
            }
        }
        return result;
    }
    /**
     * Static method that creates a new instance of the CloudflareVectorizeStore class
     * from texts.
     * @param texts Array of texts to add to the Vectorize database.
     * @param metadatas Metadata associated with the texts.
     * @param embeddings Embeddings to use for the texts.
     * @param dbConfig Configuration for the Vectorize database.
     * @param options Optional ids for the vectors.
     * @returns Promise that resolves with a new instance of the CloudflareVectorizeStore class.
     */
    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 newDoc = new documents_1.Document({
                pageContent: texts[i],
                metadata,
            });
            docs.push(newDoc);
        }
        return CloudflareVectorizeStore.fromDocuments(docs, embeddings, dbConfig);
    }
    /**
     * Static method that creates a new instance of the CloudflareVectorizeStore class
     * from documents.
     * @param docs Array of documents to add to the Vectorize database.
     * @param embeddings Embeddings to use for the documents.
     * @param dbConfig Configuration for the Vectorize database.
     * @param options Optional ids for the vectors.
     * @returns Promise that resolves with a new instance of the CloudflareVectorizeStore class.
     */
    static async fromDocuments(docs, embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * Static method that creates a new instance of the CloudflareVectorizeStore class
     * from an existing index.
     * @param embeddings Embeddings to use for the documents.
     * @param dbConfig Configuration for the Vectorize database.
     * @returns Promise that resolves with a new instance of the CloudflareVectorizeStore class.
     */
    static async fromExistingIndex(embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        return instance;
    }
}
exports.CloudflareVectorizeStore = CloudflareVectorizeStore;
