"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
exports.MyScaleStore = void 0;
const uuid = __importStar(require("uuid"));
const client_1 = require("@clickhouse/client");
const vectorstores_1 = require("@langchain/core/vectorstores");
const documents_1 = require("@langchain/core/documents");
/**
 * Class for interacting with the MyScale database. It extends the
 * VectorStore class and provides methods for adding vectors and
 * documents, searching for similar vectors, and creating instances from
 * texts or documents.
 */
class MyScaleStore extends vectorstores_1.VectorStore {
    _vectorstoreType() {
        return "myscale";
    }
    constructor(embeddings, args) {
        super(embeddings, args);
        Object.defineProperty(this, "client", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "indexType", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "indexParam", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "columnMap", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "database", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "table", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "metric", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "isInitialized", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: false
        });
        this.indexType = args.indexType || "MSTG";
        this.indexParam = args.indexParam || {};
        this.columnMap = args.columnMap || {
            id: "id",
            text: "text",
            vector: "vector",
            metadata: "metadata",
        };
        this.database = args.database || "default";
        this.table = args.table || "vector_table";
        this.metric = args.metric || "Cosine";
        this.client = (0, client_1.createClient)({
            host: `${args.protocol ?? "https://"}${args.host}:${args.port}`,
            username: args.username,
            password: args.password,
            session_id: uuid.v4(),
        });
    }
    /**
     * Method to add vectors to the MyScale database.
     * @param vectors The vectors to add.
     * @param documents The documents associated with the vectors.
     * @returns Promise that resolves when the vectors have been added.
     */
    async addVectors(vectors, documents) {
        if (vectors.length === 0) {
            return;
        }
        if (!this.isInitialized) {
            await this.initialize(vectors[0].length);
        }
        const queryStr = this.buildInsertQuery(vectors, documents);
        await this.client.exec({ query: queryStr });
    }
    /**
     * Method to add documents to the MyScale database.
     * @param documents The documents to add.
     * @returns Promise that resolves when the documents have been added.
     */
    async addDocuments(documents) {
        return this.addVectors(await this.embeddings.embedDocuments(documents.map((d) => d.pageContent)), documents);
    }
    /**
     * Method to search for vectors that are similar to a given query vector.
     * @param query The query vector.
     * @param k The number of similar vectors to return.
     * @param filter Optional filter for the search results.
     * @returns Promise that resolves with an array of tuples, each containing a Document and a score.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        if (!this.isInitialized) {
            await this.initialize(query.length);
        }
        const queryStr = this.buildSearchQuery(query, k, filter);
        const queryResultSet = await this.client.query({ query: queryStr });
        const queryResult = await queryResultSet.json();
        const result = queryResult.data.map((item) => [
            new documents_1.Document({ pageContent: item.text, metadata: item.metadata }),
            item.dist,
        ]);
        return result;
    }
    /**
     * Static method to create an instance of MyScaleStore from texts.
     * @param texts The texts to use.
     * @param metadatas The metadata associated with the texts.
     * @param embeddings The embeddings to use.
     * @param args The arguments for the MyScaleStore.
     * @returns Promise that resolves with a new instance of MyScaleStore.
     */
    static async 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 MyScaleStore.fromDocuments(docs, embeddings, args);
    }
    /**
     * Static method to create an instance of MyScaleStore from documents.
     * @param docs The documents to use.
     * @param embeddings The embeddings to use.
     * @param args The arguments for the MyScaleStore.
     * @returns Promise that resolves with a new instance of MyScaleStore.
     */
    static async fromDocuments(docs, embeddings, args) {
        const instance = new this(embeddings, args);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * Static method to create an instance of MyScaleStore from an existing
     * index.
     * @param embeddings The embeddings to use.
     * @param args The arguments for the MyScaleStore.
     * @returns Promise that resolves with a new instance of MyScaleStore.
     */
    static async fromExistingIndex(embeddings, args) {
        const instance = new this(embeddings, args);
        await instance.initialize();
        return instance;
    }
    /**
     * Method to initialize the MyScale database.
     * @param dimension Optional dimension of the vectors.
     * @returns Promise that resolves when the database has been initialized.
     */
    async initialize(dimension) {
        const dim = dimension ?? (await this.embeddings.embedQuery("test")).length;
        let indexParamStr = "";
        for (const [key, value] of Object.entries(this.indexParam)) {
            indexParamStr += `, '${key}=${value}'`;
        }
        const query = `
      CREATE TABLE IF NOT EXISTS ${this.database}.${this.table}(
        ${this.columnMap.id} String,
        ${this.columnMap.text} String,
        ${this.columnMap.vector} Array(Float32),
        ${this.columnMap.metadata} JSON,
        CONSTRAINT cons_vec_len CHECK length(${this.columnMap.vector}) = ${dim},
        VECTOR INDEX vidx ${this.columnMap.vector} TYPE ${this.indexType}('metric_type=${this.metric}'${indexParamStr})
      ) ENGINE = MergeTree ORDER BY ${this.columnMap.id}
    `;
        await this.client.exec({ query: "SET allow_experimental_object_type=1" });
        await this.client.exec({
            query: "SET output_format_json_named_tuples_as_objects = 1",
        });
        await this.client.exec({ query });
        this.isInitialized = true;
    }
    /**
     * Method to build an SQL query for inserting vectors and documents into
     * the MyScale database.
     * @param vectors The vectors to insert.
     * @param documents The documents to insert.
     * @returns The SQL query string.
     */
    buildInsertQuery(vectors, documents) {
        const columnsStr = Object.values(this.columnMap).join(", ");
        const data = [];
        for (let i = 0; i < vectors.length; i += 1) {
            const vector = vectors[i];
            const document = documents[i];
            const item = [
                `'${uuid.v4()}'`,
                `'${this.escapeString(document.pageContent)}'`,
                `[${vector}]`,
                `'${JSON.stringify(document.metadata)}'`,
            ].join(", ");
            data.push(`(${item})`);
        }
        const dataStr = data.join(", ");
        return `
      INSERT INTO TABLE
        ${this.database}.${this.table}(${columnsStr})
      VALUES
        ${dataStr}
    `;
    }
    escapeString(str) {
        return str.replace(/\\/g, "\\\\").replace(/'/g, "\\'");
    }
    /**
     * Method to build an SQL query for searching for similar vectors in the
     * MyScale database.
     * @param query The query vector.
     * @param k The number of similar vectors to return.
     * @param filter Optional filter for the search results.
     * @returns The SQL query string.
     */
    buildSearchQuery(query, k, filter) {
        const order = this.metric === "IP" ? "DESC" : "ASC";
        const whereStr = filter ? `PREWHERE ${filter.whereStr}` : "";
        return `
      SELECT ${this.columnMap.text} AS text, ${this.columnMap.metadata} AS metadata, dist
      FROM ${this.database}.${this.table}
      ${whereStr}
      ORDER BY distance(${this.columnMap.vector}, [${query}]) AS dist ${order}
      LIMIT ${k}
    `;
    }
}
exports.MyScaleStore = MyScaleStore;
