"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
exports.PineconeStore = void 0;
/* eslint-disable no-process-env */
const uuid = __importStar(require("uuid"));
const flat_1 = __importDefault(require("flat"));
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 math_1 = require("@langchain/core/utils/math");
const chunk_array_1 = require("@langchain/core/utils/chunk_array");
/**
 * @deprecated Install and import from @langchain/pinecone instead.
 * Class that extends the VectorStore class and provides methods to
 * interact with the Pinecone vector database.
 */
class PineconeStore extends vectorstores_1.VectorStore {
    _vectorstoreType() {
        return "pinecone";
    }
    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, "pineconeIndex", {
            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, "caller", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.embeddings = embeddings;
        const { namespace, pineconeIndex, textKey, filter, ...asyncCallerArgs } = args;
        this.namespace = namespace;
        this.pineconeIndex = pineconeIndex;
        this.textKey = textKey ?? "text";
        this.filter = filter;
        this.caller = new async_caller_1.AsyncCaller(asyncCallerArgs);
    }
    /**
     * Method that adds documents to the Pinecone database.
     * @param documents Array of documents to add to the Pinecone database.
     * @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 Pinecone database.
     * @param vectors Array of vectors to add to the Pinecone database.
     * @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 pineconeVectors = vectors.map((values, idx) => {
            // Pinecone doesn't support nested objects, so we flatten them
            const documentMetadata = { ...documents[idx].metadata };
            // preserve string arrays which are allowed
            const stringArrays = {};
            for (const key of Object.keys(documentMetadata)) {
                if (Array.isArray(documentMetadata[key]) &&
                    // eslint-disable-next-line @typescript-eslint/ban-types, @typescript-eslint/no-explicit-any
                    documentMetadata[key].every((el) => typeof el === "string")) {
                    stringArrays[key] = documentMetadata[key];
                    delete documentMetadata[key];
                }
            }
            const metadata = {
                ...(0, flat_1.default)(documentMetadata),
                ...stringArrays,
                [this.textKey]: documents[idx].pageContent,
            };
            // Pinecone doesn't support null values, so we remove them
            for (const key of Object.keys(metadata)) {
                if (metadata[key] == null) {
                    delete metadata[key];
                }
                else if (typeof metadata[key] === "object" &&
                    Object.keys(metadata[key]).length === 0) {
                    delete metadata[key];
                }
            }
            return {
                id: documentIds[idx],
                metadata,
                values,
            };
        });
        const namespace = this.pineconeIndex.namespace(this.namespace ?? "");
        // Pinecone recommends a limit of 100 vectors per upsert request
        const chunkSize = 100;
        const chunkedVectors = (0, chunk_array_1.chunkArray)(pineconeVectors, chunkSize);
        const batchRequests = chunkedVectors.map((chunk) => this.caller.call(async () => namespace.upsert(chunk)));
        await Promise.all(batchRequests);
        return documentIds;
    }
    /**
     * Method that deletes vectors from the Pinecone database.
     * @param params Parameters for the delete operation.
     * @returns Promise that resolves when the delete operation is complete.
     */
    async delete(params) {
        const { deleteAll, ids, filter } = params;
        const namespace = this.pineconeIndex.namespace(this.namespace ?? "");
        if (deleteAll) {
            await namespace.deleteAll();
        }
        else if (ids) {
            const batchSize = 1000;
            for (let i = 0; i < ids.length; i += batchSize) {
                const batchIds = ids.slice(i, i + batchSize);
                await namespace.deleteMany(batchIds);
            }
        }
        else if (filter) {
            await namespace.deleteMany(filter);
        }
        else {
            throw new Error("Either ids or delete_all must be provided.");
        }
    }
    async _runPineconeQuery(query, k, filter, options) {
        if (filter && this.filter) {
            throw new Error("cannot provide both `filter` and `this.filter`");
        }
        const _filter = filter ?? this.filter;
        const namespace = this.pineconeIndex.namespace(this.namespace ?? "");
        const results = await namespace.query({
            includeMetadata: true,
            topK: k,
            vector: query,
            filter: _filter,
            ...options,
        });
        return results;
    }
    /**
     * Method that performs a similarity search in the Pinecone 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.
     * @param filter Optional filter to apply to the search.
     * @returns Promise that resolves with an array of documents and their scores.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        const results = await this._runPineconeQuery(query, k, filter);
        const result = [];
        if (results.matches) {
            for (const res of results.matches) {
                const { [this.textKey]: pageContent, ...metadata } = (res.metadata ??
                    {});
                if (res.score) {
                    result.push([new documents_1.Document({ metadata, pageContent }), res.score]);
                }
            }
        }
        return result;
    }
    /**
     * 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=20 - Number of documents to fetch before passing to the MMR algorithm.
     * @param {number} 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.
     * @param {PineconeMetadata} options.filter - Optional filter to apply to the search.
     *
     * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
     */
    async maxMarginalRelevanceSearch(query, options) {
        const queryEmbedding = await this.embeddings.embedQuery(query);
        const results = await this._runPineconeQuery(queryEmbedding, options.fetchK ?? 20, options.filter, { includeValues: true });
        const matches = results?.matches ?? [];
        const embeddingList = matches.map((match) => match.values);
        const mmrIndexes = (0, math_1.maximalMarginalRelevance)(queryEmbedding, embeddingList, options.lambda, options.k);
        const topMmrMatches = mmrIndexes.map((idx) => matches[idx]);
        const finalResult = [];
        for (const res of topMmrMatches) {
            const { [this.textKey]: pageContent, ...metadata } = (res.metadata ??
                {});
            if (res.score) {
                finalResult.push(new documents_1.Document({ metadata, pageContent }));
            }
        }
        return finalResult;
    }
    /**
     * Static method that creates a new instance of the PineconeStore class
     * from texts.
     * @param texts Array of texts to add to the Pinecone database.
     * @param metadatas Metadata associated with the texts.
     * @param embeddings Embeddings to use for the texts.
     * @param dbConfig Configuration for the Pinecone database.
     * @returns Promise that resolves with a new instance of the PineconeStore 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);
        }
        const args = {
            pineconeIndex: dbConfig.pineconeIndex,
            textKey: dbConfig.textKey,
            namespace: dbConfig.namespace,
        };
        return PineconeStore.fromDocuments(docs, embeddings, args);
    }
    /**
     * Static method that creates a new instance of the PineconeStore class
     * from documents.
     * @param docs Array of documents to add to the Pinecone database.
     * @param embeddings Embeddings to use for the documents.
     * @param dbConfig Configuration for the Pinecone database.
     * @returns Promise that resolves with a new instance of the PineconeStore class.
     */
    static async fromDocuments(docs, embeddings, dbConfig) {
        const args = dbConfig;
        args.textKey = dbConfig.textKey ?? "text";
        const instance = new this(embeddings, args);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * Static method that creates a new instance of the PineconeStore class
     * from an existing index.
     * @param embeddings Embeddings to use for the documents.
     * @param dbConfig Configuration for the Pinecone database.
     * @returns Promise that resolves with a new instance of the PineconeStore class.
     */
    static async fromExistingIndex(embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        return instance;
    }
}
exports.PineconeStore = PineconeStore;
