"use strict";
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
    if (k2 === undefined) k2 = k;
    var desc = Object.getOwnPropertyDescriptor(m, k);
    if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
      desc = { enumerable: true, get: function() { return m[k]; } };
    }
    Object.defineProperty(o, k2, desc);
}) : (function(o, m, k, k2) {
    if (k2 === undefined) k2 = k;
    o[k2] = m[k];
}));
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
    Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
    o["default"] = v;
});
var __importStar = (this && this.__importStar) || function (mod) {
    if (mod && mod.__esModule) return mod;
    var result = {};
    if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
    __setModuleDefault(result, mod);
    return result;
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.WeaviateStore = exports.flattenObjectForWeaviate = void 0;
const uuid = __importStar(require("uuid"));
const vectorstores_1 = require("@langchain/core/vectorstores");
const documents_1 = require("@langchain/core/documents");
const math_1 = require("@langchain/core/utils/math");
// Note this function is not generic, it is designed specifically for Weaviate
// https://weaviate.io/developers/weaviate/config-refs/datatypes#introduction
/**
 * @deprecated Prefer the `@langchain/weaviate` package.
 */
const flattenObjectForWeaviate = (
// eslint-disable-next-line @typescript-eslint/no-explicit-any
obj) => {
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    const flattenedObject = {};
    for (const key in obj) {
        if (!Object.hasOwn(obj, key)) {
            continue;
        }
        const value = obj[key];
        if (typeof obj[key] === "object" && !Array.isArray(value)) {
            const recursiveResult = (0, exports.flattenObjectForWeaviate)(value);
            for (const deepKey in recursiveResult) {
                if (Object.hasOwn(obj, key)) {
                    flattenedObject[`${key}_${deepKey}`] = recursiveResult[deepKey];
                }
            }
        }
        else if (Array.isArray(value)) {
            if (value.length === 0) {
                flattenedObject[key] = value;
            }
            else if (typeof value[0] !== "object" &&
                // eslint-disable-next-line @typescript-eslint/no-explicit-any
                value.every((el) => typeof el === typeof value[0])) {
                // Weaviate only supports arrays of primitive types,
                // where all elements are of the same type
                flattenedObject[key] = value;
            }
        }
        else {
            flattenedObject[key] = value;
        }
    }
    return flattenedObject;
};
exports.flattenObjectForWeaviate = flattenObjectForWeaviate;
/**
 * @deprecated Prefer the `@langchain/weaviate` package.
 *
 * Class that extends the `VectorStore` base class. It provides methods to
 * interact with a Weaviate index, including adding vectors and documents,
 * deleting data, and performing similarity searches.
 */
class WeaviateStore extends vectorstores_1.VectorStore {
    _vectorstoreType() {
        return "weaviate";
    }
    constructor(embeddings, args) {
        super(embeddings, args);
        Object.defineProperty(this, "embeddings", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: embeddings
        });
        Object.defineProperty(this, "client", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "indexName", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "textKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "queryAttrs", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "tenant", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.client = args.client;
        this.indexName = args.indexName;
        this.textKey = args.textKey || "text";
        this.queryAttrs = [this.textKey];
        this.tenant = args.tenant;
        if (args.metadataKeys) {
            this.queryAttrs = [
                ...new Set([
                    ...this.queryAttrs,
                    ...args.metadataKeys.filter((k) => {
                        // https://spec.graphql.org/June2018/#sec-Names
                        // queryAttrs need to be valid GraphQL Names
                        const keyIsValid = /^[_A-Za-z][_0-9A-Za-z]*$/.test(k);
                        if (!keyIsValid) {
                            console.warn(`Skipping metadata key ${k} as it is not a valid GraphQL Name`);
                        }
                        return keyIsValid;
                    }),
                ]),
            ];
        }
    }
    /**
     * Method to add vectors and corresponding documents to the Weaviate
     * index.
     * @param vectors Array of vectors to be added.
     * @param documents Array of documents corresponding to the vectors.
     * @param options Optional parameter that can include specific IDs for the documents.
     * @returns An array of document IDs.
     */
    async addVectors(vectors, documents, options) {
        const documentIds = options?.ids ?? documents.map((_) => uuid.v4());
        const batch = documents.map((document, index) => {
            if (Object.hasOwn(document.metadata, "id"))
                throw new Error("Document inserted to Weaviate vectorstore should not have `id` in their metadata.");
            const flattenedMetadata = (0, exports.flattenObjectForWeaviate)(document.metadata);
            return {
                ...(this.tenant ? { tenant: this.tenant } : {}),
                class: this.indexName,
                id: documentIds[index],
                vector: vectors[index],
                properties: {
                    [this.textKey]: document.pageContent,
                    ...flattenedMetadata,
                },
            };
        });
        try {
            const responses = await this.client.batch
                .objectsBatcher()
                .withObjects(...batch)
                .do();
            // if storing vectors fails, we need to know why
            const errorMessages = [];
            responses.forEach((response) => {
                if (response?.result?.errors?.error) {
                    errorMessages.push(...response.result.errors.error.map((err) => err.message ??
                        "!! Unfortunately no error message was presented in the API response !!"));
                }
            });
            if (errorMessages.length > 0) {
                throw new Error(errorMessages.join("\n"));
            }
        }
        catch (e) {
            throw Error(`Error adding vectors: ${e}`);
        }
        return documentIds;
    }
    /**
     * Method to add documents to the Weaviate index. It first generates
     * vectors for the documents using the embeddings, then adds the vectors
     * and documents to the index.
     * @param documents Array of documents to be added.
     * @param options Optional parameter that can include specific IDs for the documents.
     * @returns An array of document IDs.
     */
    async addDocuments(documents, options) {
        return this.addVectors(await this.embeddings.embedDocuments(documents.map((d) => d.pageContent)), documents, options);
    }
    /**
     * Method to delete data from the Weaviate index. It can delete data based
     * on specific IDs or a filter.
     * @param params Object that includes either an array of IDs or a filter for the data to be deleted.
     * @returns Promise that resolves when the deletion is complete.
     */
    async delete(params) {
        const { ids, filter } = params;
        if (ids && ids.length > 0) {
            for (const id of ids) {
                let deleter = this.client.data
                    .deleter()
                    .withClassName(this.indexName)
                    .withId(id);
                if (this.tenant) {
                    deleter = deleter.withTenant(this.tenant);
                }
                await deleter.do();
            }
        }
        else if (filter) {
            let batchDeleter = this.client.batch
                .objectsBatchDeleter()
                .withClassName(this.indexName)
                .withWhere(filter.where);
            if (this.tenant) {
                batchDeleter = batchDeleter.withTenant(this.tenant);
            }
            await batchDeleter.do();
        }
        else {
            throw new Error(`This method requires either "ids" or "filter" to be set in the input object`);
        }
    }
    /**
     * Method to perform a similarity search on the stored vectors in the
     * Weaviate index. It returns the top k most similar documents and their
     * similarity scores.
     * @param query The query vector.
     * @param k The number of most similar documents to return.
     * @param filter Optional filter to apply to the search.
     * @returns An array of tuples, where each tuple contains a document and its similarity score.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        const resultsWithEmbedding = await this.similaritySearchVectorWithScoreAndEmbedding(query, k, filter);
        return resultsWithEmbedding.map(([document, score, _embedding]) => [
            document,
            score,
        ]);
    }
    /**
     * Method to perform a similarity search on the stored vectors in the
     * Weaviate index. It returns the top k most similar documents, their
     * similarity scores and embedding vectors.
     * @param query The query vector.
     * @param k The number of most similar documents to return.
     * @param filter Optional filter to apply to the search.
     * @returns An array of tuples, where each tuple contains a document, its similarity score and its embedding vector.
     */
    async similaritySearchVectorWithScoreAndEmbedding(query, k, filter) {
        try {
            let builder = this.client.graphql
                .get()
                .withClassName(this.indexName)
                .withFields(`${this.queryAttrs.join(" ")} _additional { distance vector }`)
                .withNearVector({
                vector: query,
                distance: filter?.distance,
            })
                .withLimit(k);
            if (this.tenant) {
                builder = builder.withTenant(this.tenant);
            }
            if (filter?.where) {
                builder = builder.withWhere(filter.where);
            }
            const result = await builder.do();
            const documents = [];
            for (const data of result.data.Get[this.indexName]) {
                const { [this.textKey]: text, _additional, ...rest } = data;
                documents.push([
                    new documents_1.Document({
                        pageContent: text,
                        metadata: rest,
                    }),
                    _additional.distance,
                    _additional.vector,
                ]);
            }
            return documents;
        }
        catch (e) {
            throw Error(`'Error in similaritySearch' ${e}`);
        }
    }
    /**
     * Return documents selected using the maximal marginal relevance.
     * Maximal marginal relevance optimizes for similarity to the query AND diversity
     * among selected documents.
     *
     * @param {string} query - Text to look up documents similar to.
     * @param {number} options.k - Number of documents to return.
     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
     * @param {number} options.lambda - 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.
     * @param {this["FilterType"]} options.filter - Optional filter
     * @param _callbacks
     *
     * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
     */
    async maxMarginalRelevanceSearch(query, options, _callbacks) {
        const { k, fetchK = 20, lambda = 0.5, filter } = options;
        const queryEmbedding = await this.embeddings.embedQuery(query);
        const allResults = await this.similaritySearchVectorWithScoreAndEmbedding(queryEmbedding, fetchK, filter);
        const embeddingList = allResults.map(([_doc, _score, embedding]) => embedding);
        const mmrIndexes = (0, math_1.maximalMarginalRelevance)(queryEmbedding, embeddingList, lambda, k);
        return mmrIndexes
            .filter((idx) => idx !== -1)
            .map((idx) => allResults[idx][0]);
    }
    /**
     * Static method to create a new `WeaviateStore` instance from a list of
     * texts. It first creates documents from the texts and metadata, then
     * adds the documents to the Weaviate index.
     * @param texts Array of texts.
     * @param metadatas Metadata for the texts. Can be a single object or an array of objects.
     * @param embeddings Embeddings to be used for the texts.
     * @param args Arguments required to create a new `WeaviateStore` instance.
     * @returns A new `WeaviateStore` instance.
     */
    static fromTexts(texts, metadatas, embeddings, args) {
        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 WeaviateStore.fromDocuments(docs, embeddings, args);
    }
    /**
     * Static method to create a new `WeaviateStore` instance from a list of
     * documents. It adds the documents to the Weaviate index.
     * @param docs Array of documents.
     * @param embeddings Embeddings to be used for the documents.
     * @param args Arguments required to create a new `WeaviateStore` instance.
     * @returns A new `WeaviateStore` instance.
     */
    static async fromDocuments(docs, embeddings, args) {
        const instance = new this(embeddings, args);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * Static method to create a new `WeaviateStore` instance from an existing
     * Weaviate index.
     * @param embeddings Embeddings to be used for the Weaviate index.
     * @param args Arguments required to create a new `WeaviateStore` instance.
     * @returns A new `WeaviateStore` instance.
     */
    static async fromExistingIndex(embeddings, args) {
        return new this(embeddings, args);
    }
}
exports.WeaviateStore = WeaviateStore;